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Програмування","\u002Fcsharp\u002Fsystem-programming-windows\u002Fasync-fundamentals","01.csharp\u002F07.system-programming-windows\u002F12.async-fundamentals",{"title":344,"path":345,"stem":346},"SynchronizationContext та ConfigureAwait — Контекст Виконання","\u002Fcsharp\u002Fsystem-programming-windows\u002Fasync-context-configureawait","01.csharp\u002F07.system-programming-windows\u002F13.async-context-configureawait",{"title":348,"path":349,"stem":350},"Async — Просунуті Паттерни","\u002Fcsharp\u002Fsystem-programming-windows\u002Fasync-advanced","01.csharp\u002F07.system-programming-windows\u002F14.async-advanced",{"title":352,"path":353,"stem":354},"System.Threading.Channels — Async Producer-Consumer","\u002Fcsharp\u002Fsystem-programming-windows\u002Fchannels","01.csharp\u002F07.system-programming-windows\u002F15.channels",{"title":356,"path":357,"stem":358},"Асинхронна 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ОС","\u002Fcsharp\u002Fsystem-programming-windows\u002Fwindows-hooks-services","01.csharp\u002F07.system-programming-windows\u002F20.windows-hooks-services",{"title":376,"path":377,"stem":378},"Системне Програмування C# (Windows) — 07.system-programming-windows","\u002Fcsharp\u002Fsystem-programming-windows\u002Fimplementation_plan","01.csharp\u002F07.system-programming-windows\u002Fimplementation_plan",{"title":380,"icon":132,"path":381,"stem":382,"children":383,"page":59},"Io","\u002Fcsharp\u002Fio","01.csharp\u002F08.io",[384,388,392,396,400],{"title":385,"path":386,"stem":387},"8.1.1. Основи роботи з файловою системою","\u002Fcsharp\u002Fio\u002Ffile-system-basics","01.csharp\u002F08.io\u002F01.file-system-basics",{"title":389,"path":390,"stem":391},"8.1.2. Потоки (Streams) та Серіалізація Даних","\u002Fcsharp\u002Fio\u002Fstreams-serialization","01.csharp\u002F08.io\u002F02.streams-serialization",{"title":393,"path":394,"stem":395},"8.2.1. JSON Serialization з System.Text.Json","\u002Fcsharp\u002Fio\u002Fjson-serialization","01.csharp\u002F08.io\u002F03.json-serialization",{"title":397,"path":398,"stem":399},"8.2.2. XML Serialization та LINQ to XML","\u002Fcsharp\u002Fio\u002Fxml-serialization","01.csharp\u002F08.io\u002F04.xml-serialization",{"title":401,"path":402,"stem":403},"8.2.3. Binary Serialization: MessagePack та Protocol Buffers","\u002Fcsharp\u002Fio\u002Fbinary-serialization","01.csharp\u002F08.io\u002F05.binary-serialization",{"title":405,"icon":132,"path":406,"stem":407,"children":408,"page":59},"Ado Net","\u002Fcsharp\u002Fado-net","01.csharp\u002F09.ado-net",[409,413,417,421,425,429,433,437,441,445,449,453],{"title":410,"path":411,"stem":412},"9.1. Введення в ADO.NET","\u002Fcsharp\u002Fado-net\u002Fintroduction-to-adonet","01.csharp\u002F09.ado-net\u002F01.introduction-to-adonet",{"title":414,"path":415,"stem":416},"9.2. Клас DbConnection — з'єднання з базою даних","\u002Fcsharp\u002Fado-net\u002Fconnection","01.csharp\u002F09.ado-net\u002F02.connection",{"title":418,"path":419,"stem":420},"9.3. Клас DbCommand — виконання SQL-запитів","\u002Fcsharp\u002Fado-net\u002Fcommand-and-queries","01.csharp\u002F09.ado-net\u002F03.command-and-queries",{"title":422,"path":423,"stem":424},"9.4. Клас DbDataReader — ефективне читання даних","\u002Fcsharp\u002Fado-net\u002Fdatareader","01.csharp\u002F09.ado-net\u002F04.datareader",{"title":426,"path":427,"stem":428},"9.5. Параметризовані запити та захист від SQL Injection","\u002Fcsharp\u002Fado-net\u002Fparameters-and-sql-injection","01.csharp\u002F09.ado-net\u002F05.parameters-and-sql-injection",{"title":430,"path":431,"stem":432},"9.6. Транзакції в ADO.NET","\u002Fcsharp\u002Fado-net\u002Ftransactions","01.csharp\u002F09.ado-net\u002F06.transactions",{"title":434,"path":435,"stem":436},"9.7. DbProviderFactory — провайдер-незалежний код","\u002Fcsharp\u002Fado-net\u002Fprovider-factory","01.csharp\u002F09.ado-net\u002F07.provider-factory",{"title":438,"path":439,"stem":440},"9.8. Асинхронний доступ до даних","\u002Fcsharp\u002Fado-net\u002Fasync-data-access","01.csharp\u002F09.ado-net\u002F08.async-data-access",{"title":442,"path":443,"stem":444},"9.9. Від'єднаний режим: DataSet, DataTable, DataRow","\u002Fcsharp\u002Fado-net\u002Fdisconnected-mode-dataset","01.csharp\u002F09.ado-net\u002F09.disconnected-mode-dataset",{"title":446,"path":447,"stem":448},"9.10. DataAdapter — міст між DataSet та базою даних","\u002Fcsharp\u002Fado-net\u002Fdata-adapter","01.csharp\u002F09.ado-net\u002F10.data-adapter",{"title":450,"path":451,"stem":452},"9.11. Data Mapper та Repository: Архітектура доступу до даних","\u002Fcsharp\u002Fado-net\u002Fdata-mapper-repository","01.csharp\u002F09.ado-net\u002F11.data-mapper-repository",{"title":454,"path":455,"stem":456},"9.12. Identity Map, Unit of Work та Specification Pattern","\u002Fcsharp\u002Fado-net\u002Fadvanced-patterns","01.csharp\u002F09.ado-net\u002F12.advanced-patterns",{"title":458,"icon":255,"path":459,"stem":460,"children":461,"page":59},"Ef Core","\u002Fcsharp\u002Fef-core","01.csharp\u002F10.ef-core",[462,466,470,474,478,482,486,490,494,498,502,506,510,514,518,522,526,532,538,542,546,550,554,558,562,566,570,574,578,582,586,590,594,598,602,606,610,614,618,622,626,630,634,638,642,646],{"title":463,"path":464,"stem":465},"Що таке ORM? Від SQL до об'єктів","\u002Fcsharp\u002Fef-core\u002Fwhat-is-orm","01.csharp\u002F10.ef-core\u002F01.what-is-orm",{"title":467,"path":468,"stem":469},"Перший проєкт — від нуля до CRUD","\u002Fcsharp\u002Fef-core\u002Ffirst-project","01.csharp\u002F10.ef-core\u002F02.first-project",{"title":471,"path":472,"stem":473},"DbContext — Серце EF Core","\u002Fcsharp\u002Fef-core\u002Fdbcontext-deep-dive","01.csharp\u002F10.ef-core\u002F03.dbcontext-deep-dive",{"title":475,"path":476,"stem":477},"Провайдери баз даних — Архітектура та Вибір СУБД","\u002Fcsharp\u002Fef-core\u002Fdatabase-providers","01.csharp\u002F10.ef-core\u002F04.database-providers",{"title":479,"path":480,"stem":481},"Конвенції EF Core — Магія без конфігурації","\u002Fcsharp\u002Fef-core\u002Fconventions","01.csharp\u002F10.ef-core\u002F05.conventions",{"title":483,"path":484,"stem":485},"Fluent API та Data Annotations — Явна конфігурація моделі","\u002Fcsharp\u002Fef-core\u002Ffluent-api-vs-annotations","01.csharp\u002F10.ef-core\u002F06.fluent-api-vs-annotations",{"title":487,"path":488,"stem":489},"Зв'язки — One-to-One та One-to-Many","\u002Fcsharp\u002Fef-core\u002Frelationships-basics","01.csharp\u002F10.ef-core\u002F07.relationships-basics",{"title":491,"path":492,"stem":493},"Зв'язки Advanced — Many-to-Many та Складні Сценарії","\u002Fcsharp\u002Fef-core\u002Frelationships-advanced","01.csharp\u002F10.ef-core\u002F08.relationships-advanced",{"title":495,"path":496,"stem":497},"Властивості — Типи, Конвертери, Компаратори (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fproperty-configuration-part1","01.csharp\u002F10.ef-core\u002F09.property-configuration-part1",{"title":499,"path":500,"stem":501},"Властивості — Value Comparers, Generators, Shadow Properties (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fproperty-configuration-part2","01.csharp\u002F10.ef-core\u002F09.property-configuration-part2",{"title":503,"path":504,"stem":505},"Складні типи — Owned Types та Complex Types (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fcomplex-types-owned-part1","01.csharp\u002F10.ef-core\u002F10.complex-types-owned-part1",{"title":507,"path":508,"stem":509},"Складні типи — Complex Types, Keyless Entities, Порівняння (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fcomplex-types-owned-part2","01.csharp\u002F10.ef-core\u002F10.complex-types-owned-part2",{"title":511,"path":512,"stem":513},"JSON Columns — Складні дані у JSON (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fjson-columns-part1","01.csharp\u002F10.ef-core\u002F11.json-columns-part1",{"title":515,"path":516,"stem":517},"JSON Columns — Value Comparers, Індекси, Провайдери (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fjson-columns-part2","01.csharp\u002F10.ef-core\u002F11.json-columns-part2",{"title":519,"path":520,"stem":521},"Успадкування — Абстрактні класи та TPH (Частина 1)","\u002Fcsharp\u002Fef-core\u002Finheritance-part1","01.csharp\u002F10.ef-core\u002F12.inheritance-part1",{"title":523,"path":524,"stem":525},"Успадкування — TPT, TPC та Порівняння Стратегій (Частина 2)","\u002Fcsharp\u002Fef-core\u002Finheritance-part2","01.csharp\u002F10.ef-core\u002F12.inheritance-part2",{"title":527,"path":528,"stem":529,"children":530},"Індекси, Обмеження та Схема (Частина 1)","\u002Fcsharp\u002Fef-core\u002Findexes-constraints-part1","01.csharp\u002F10.ef-core\u002F13.indexes-constraints-part1",[531],{"title":527,"path":528,"stem":529},{"title":533,"path":534,"stem":535,"children":536},"Індекси, Обмеження та Схема (Частина 2)","\u002Fcsharp\u002Fef-core\u002Findexes-constraints-part2","01.csharp\u002F10.ef-core\u002F13.indexes-constraints-part2",[537],{"title":533,"path":534,"stem":535},{"title":539,"path":540,"stem":541},"Seed Data — Початкові Дані (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fseeding-part1","01.csharp\u002F10.ef-core\u002F14.seeding-part1",{"title":543,"path":544,"stem":545},"Seed Data — SQL-скрипти, Bogus та Стратегії (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fseeding-part2","01.csharp\u002F10.ef-core\u002F14.seeding-part2",{"title":547,"path":548,"stem":549},"Global Query Filters — Глобальні Фільтри (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fglobal-query-filters-part1","01.csharp\u002F10.ef-core\u002F15.global-query-filters-part1",{"title":551,"path":552,"stem":553},"Global Query Filters — Підводні камені та Інтеграція (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fglobal-query-filters-part2","01.csharp\u002F10.ef-core\u002F15.global-query-filters-part2",{"title":555,"path":556,"stem":557},"LINQ-запити в EF Core (Частина 1)","\u002Fcsharp\u002Fef-core\u002Flinq-queries-part1","01.csharp\u002F10.ef-core\u002F16.linq-queries-part1",{"title":559,"path":560,"stem":561},"LINQ-запити в EF Core (Частина 2)","\u002Fcsharp\u002Fef-core\u002Flinq-queries-part2","01.csharp\u002F10.ef-core\u002F16.linq-queries-part2",{"title":563,"path":564,"stem":565},"Завантаження Пов'язаних Даних (Частина 1)","\u002Fcsharp\u002Fef-core\u002Floading-related-data-part1","01.csharp\u002F10.ef-core\u002F17.loading-related-data-part1",{"title":567,"path":568,"stem":569},"Завантаження Пов'язаних Даних (Частина 2)","\u002Fcsharp\u002Fef-core\u002Floading-related-data-part2","01.csharp\u002F10.ef-core\u002F17.loading-related-data-part2",{"title":571,"path":572,"stem":573},"Raw SQL, Views та Stored Procedures (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fraw-sql-part1","01.csharp\u002F10.ef-core\u002F18.raw-sql-part1",{"title":575,"path":576,"stem":577},"Raw SQL — Stored Procedures, DbFunction та Bulk Operations (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fraw-sql-part2","01.csharp\u002F10.ef-core\u002F18.raw-sql-part2",{"title":579,"path":580,"stem":581},"Продвинуті Запити — Compiled Queries, Bulk та Оптимізація (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fadvanced-queries-part1","01.csharp\u002F10.ef-core\u002F19.advanced-queries-part1",{"title":583,"path":584,"stem":585},"Продвинуті Запити — Query Tags, Bulk та Interceptors (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fadvanced-queries-part2","01.csharp\u002F10.ef-core\u002F19.advanced-queries-part2",{"title":587,"path":588,"stem":589},"Change Tracker — Відстеження Змін (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fchange-tracking-part1","01.csharp\u002F10.ef-core\u002F20.change-tracking-part1",{"title":591,"path":592,"stem":593},"Change Tracker — Графи Об'єктів та Disconnected (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fchange-tracking-part2","01.csharp\u002F10.ef-core\u002F20.change-tracking-part2",{"title":595,"path":596,"stem":597},"Збереження Даних та Транзакції (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fsaving-data-part1","01.csharp\u002F10.ef-core\u002F21.saving-data-part1",{"title":599,"path":600,"stem":601},"Збереження Даних — Concurrency та Outbox (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fsaving-data-part2","01.csharp\u002F10.ef-core\u002F21.saving-data-part2",{"title":603,"path":604,"stem":605},"Конкурентність та Блокування (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fconcurrency-part1","01.csharp\u002F10.ef-core\u002F22.concurrency-part1",{"title":607,"path":608,"stem":609},"Конкурентність — Дедлоки та Queue Processing (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fconcurrency-part2","01.csharp\u002F10.ef-core\u002F22.concurrency-part2",{"title":611,"path":612,"stem":613},"Міграції в EF Core — Основи (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fmigrations-basics-part1","01.csharp\u002F10.ef-core\u002F23.migrations-basics-part1",{"title":615,"path":616,"stem":617},"Міграції в EF Core — Основи (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fmigrations-basics-part2","01.csharp\u002F10.ef-core\u002F23.migrations-basics-part2",{"title":619,"path":620,"stem":621},"Міграції — Просунуті Сценарії (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fmigrations-advanced-part1","01.csharp\u002F10.ef-core\u002F24.migrations-advanced-part1",{"title":623,"path":624,"stem":625},"Міграції — Просунуті Сценарії (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fmigrations-advanced-part2","01.csharp\u002F10.ef-core\u002F24.migrations-advanced-part2",{"title":627,"path":628,"stem":629},"Управління Схемою та Database-First (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fschema-management-part1","01.csharp\u002F10.ef-core\u002F25.schema-management-part1",{"title":631,"path":632,"stem":633},"Управління Схемою та Database-First (Частина 2)","\u002Fcsharp\u002Fef-core\u002Fschema-management-part2","01.csharp\u002F10.ef-core\u002F25.schema-management-part2",{"title":635,"path":636,"stem":637},"Продуктивність EF Core — Основи (Частина 1)","\u002Fcsharp\u002Fef-core\u002Fperformance-fundamentals-part1","01.csharp\u002F10.ef-core\u002F26.performance-fundamentals-part1",{"title":639,"path":640,"stem":641},"Interceptors в EF Core (Частина 1)","\u002Fcsharp\u002Fef-core\u002Finterceptors-part1","01.csharp\u002F10.ef-core\u002F29.interceptors-part1",{"title":643,"path":644,"stem":645},"Interceptors в EF Core — Connection, Transaction та Materialization (Частина 2)","\u002Fcsharp\u002Fef-core\u002Finterceptors-part2","01.csharp\u002F10.ef-core\u002F29.interceptors-part2",{"title":647,"path":648,"stem":649},"План вивчення Entity Framework Core — Повний курс","\u002Fcsharp\u002Fef-core\u002Fimplementation_plan","01.csharp\u002F10.ef-core\u002Fimplementation_plan",{"title":651,"icon":652,"path":653,"stem":654,"children":655,"page":59},"ASP.NET","i-devicon-dotnetcore","\u002Fcsharp\u002Faspnet","01.csharp\u002F11.aspnet",[656,730,791,869,927,941,967,1057,1111,1182,1212,1289,1346],{"title":657,"icon":658,"path":659,"stem":660,"children":661,"page":59},"Minimal API","i-lucide-network","\u002Fcsharp\u002Faspnet\u002Fminimal-api","01.csharp\u002F11.aspnet\u002F01.minimal-api",[662,666,670,674,678,682,686,690,694,698,702,706,710,714,718,722,726],{"title":663,"path":664,"stem":665},"Вступ до ASP.NET та еволюція фреймворку","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fintroduction","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F01.introduction",{"title":667,"path":668,"stem":669},"Перший додаток на ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Ffirst-application","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F02.first-application",{"title":671,"path":672,"stem":673},"WebApplication, Builder та Dependency Injection","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fwebapplication-builder","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F03.webapplication-builder",{"title":675,"path":676,"stem":677},"Конвеєр запитів та Middleware","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Frequest-pipeline-middleware","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F04.request-pipeline-middleware",{"title":679,"path":680,"stem":681},"Маршрутизація в ASP.NET Core: Основи","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Frouting-basics","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F05.routing-basics",{"title":683,"path":684,"stem":685},"Маршрутизація в ASP.NET Core: Розширені можливості","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Frouting-advanced","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F06.routing-advanced",{"title":687,"path":688,"stem":689},"Статичні файли в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fstatic-files","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F07.static-files",{"title":691,"path":692,"stem":693},"Статичні Активи: MapStaticAssets (ASP.NET Core 9.0)","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fstatic-assets","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F08.static-assets",{"title":695,"path":696,"stem":697},"Конфігурація в ASP.NET Core: Основи","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fconfiguration-fundamentals","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F09.configuration-fundamentals",{"title":699,"path":700,"stem":701},"Конфігурація: Паттерн Options","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fconfiguration-options","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F10.configuration-options",{"title":703,"path":704,"stem":705},"Логування в ASP.NET Core: Основи","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Flogging-basics","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F11.logging-basics",{"title":707,"path":708,"stem":709},"Логування: Serilog та Middleware","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Flogging-advanced","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F12.logging-advanced",{"title":711,"path":712,"stem":713},"Управління станом: HttpContext.Items та Cookies","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fstate-management","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F13.state-management",{"title":715,"path":716,"stem":717},"Стан сесії: Sessions","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fsession-state","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F14.session-state",{"title":719,"path":720,"stem":721},"Структура проєкту: від хаосу до архітектури","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fproject-structure","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F15.project-structure",{"title":723,"path":724,"stem":725},"Scalar у Minimal API: повний проєкт і Fluent OpenAPI","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fscalar-openapi-fluent","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F16.scalar-openapi-fluent",{"title":727,"path":728,"stem":729},"Swagger \u002F Swashbuckle у Minimal API: окремий класичний шлях","\u002Fcsharp\u002Faspnet\u002Fminimal-api\u002Fswagger-swashbuckle","01.csharp\u002F11.aspnet\u002F01.minimal-api\u002F17.swagger-swashbuckle",{"title":731,"icon":658,"path":732,"stem":733,"children":734,"page":59},"API","\u002Fcsharp\u002Faspnet\u002Fapi","01.csharp\u002F11.aspnet\u002F02.api",[735,739,743,747,751,755,759,763,767,771,775,779,783,787],{"title":736,"path":737,"stem":738},"Що таке API. Клієнт-серверна архітектура","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fwhat-is-api","01.csharp\u002F11.aspnet\u002F02.api\u002F01.what-is-api",{"title":740,"path":741,"stem":742},"Формати даних: JSON, XML, TOML та бінарні формати","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fdata-formats","01.csharp\u002F11.aspnet\u002F02.api\u002F02.data-formats",{"title":744,"path":745,"stem":746},"Парадигми API та концепція REST","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fapi-paradigms-rest","01.csharp\u002F11.aspnet\u002F02.api\u002F03.api-paradigms-rest",{"title":748,"path":749,"stem":750},"HTTP-методи, статус-коди та заголовки","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fhttp-methods-status-codes","01.csharp\u002F11.aspnet\u002F02.api\u002F04.http-methods-status-codes",{"title":752,"path":753,"stem":754},"Організація HTTP API за принципами REST","\u002Fcsharp\u002Faspnet\u002Fapi\u002Frest-organizing","01.csharp\u002F11.aspnet\u002F02.api\u002F05.rest-organizing",{"title":756,"path":757,"stem":758},"Номенклатура URL та CRUD-операції","\u002Fcsharp\u002Faspnet\u002Fapi\u002Furl-nomenclature-crud","01.csharp\u002F11.aspnet\u002F02.api\u002F06.url-nomenclature-crud",{"title":760,"path":761,"stem":762},"Правила дизайну: іменування та стандарти","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fapi-design-naming","01.csharp\u002F11.aspnet\u002F02.api\u002F07.api-design-naming",{"title":764,"path":765,"stem":766},"Валідація, ліміти та обробка помилок","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fapi-design-validation","01.csharp\u002F11.aspnet\u002F02.api\u002F08.api-design-validation",{"title":768,"path":769,"stem":770},"Обробка помилок у Minimal API","\u002Fcsharp\u002Faspnet\u002Fapi\u002Ferror-handling-http","01.csharp\u002F11.aspnet\u002F02.api\u002F09.error-handling-http",{"title":772,"path":773,"stem":774},"Ідемпотентність та синхронізація стану","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fidempotency-sync","01.csharp\u002F11.aspnet\u002F02.api\u002F10.idempotency-sync",{"title":776,"path":777,"stem":778},"Пагінація та організація списків","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fpagination-lists","01.csharp\u002F11.aspnet\u002F02.api\u002F11.pagination-lists",{"title":780,"path":781,"stem":782},"Безпека API, кешування та інтернаціоналізація","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fsecurity-auth","01.csharp\u002F11.aspnet\u002F02.api\u002F12.security-auth",{"title":784,"path":785,"stem":786},"Процес проєктування API та документування","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fapi-design-process","01.csharp\u002F11.aspnet\u002F02.api\u002F13.api-design-process",{"title":788,"path":789,"stem":790},"OpenAPI: контракт, специфікація та документація API","\u002Fcsharp\u002Faspnet\u002Fapi\u002Fopenapi","01.csharp\u002F11.aspnet\u002F02.api\u002F14.openapi",{"title":792,"icon":793,"path":794,"stem":795,"children":796,"page":59},"Auth","i-lucide-shield-check","\u002Fcsharp\u002Faspnet\u002Fauth","01.csharp\u002F11.aspnet\u002F03.auth",[797,801,805,809,813,817,821,825,829,833,837,841,845,849,853,857,861,865],{"title":798,"path":799,"stem":800},"Основи аутентифікації та авторизації","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fauth-fundamentals","01.csharp\u002F11.aspnet\u002F03.auth\u002F01.auth-fundamentals",{"title":802,"path":803,"stem":804},"JWT-аутентифікація","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fjwt-authentication","01.csharp\u002F11.aspnet\u002F03.auth\u002F02.jwt-authentication",{"title":806,"path":807,"stem":808},"Авторизація: ролі, політики та resource-based доступ","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fauthorization-policies","01.csharp\u002F11.aspnet\u002F03.auth\u002F03.authorization-policies",{"title":810,"path":811,"stem":812},"Cookie-аутентифікація та ASP.NET Core Identity","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fcookie-auth-identity","01.csharp\u002F11.aspnet\u002F03.auth\u002F04.cookie-auth-identity",{"title":814,"path":815,"stem":816},"JWT + Refresh Tokens (HttpOnly Cookie)","\u002Fcsharp\u002Faspnet\u002Fauth\u002F04b.identity-auth-jwt","01.csharp\u002F11.aspnet\u002F03.auth\u002F04b.identity-auth-jwt",{"title":818,"path":819,"stem":820},"Identity: Підтвердження Email та Скидання Пароля","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fidentity-email-confirmation","01.csharp\u002F11.aspnet\u002F03.auth\u002F05.identity-email-confirmation",{"title":822,"path":823,"stem":824},"Identity: Двофакторна Аутентифікація (2FA)","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fidentity-two-factor","01.csharp\u002F11.aspnet\u002F03.auth\u002F06.identity-two-factor",{"title":826,"path":827,"stem":828},"Identity: Внутрішня Архітектура та Кастомізація","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fidentity-internals","01.csharp\u002F11.aspnet\u002F03.auth\u002F07.identity-internals",{"title":830,"path":831,"stem":832},"OAuth 2.0 та зовнішні провайдери","\u002Fcsharp\u002Faspnet\u002Fauth\u002Foauth-external-providers","01.csharp\u002F11.aspnet\u002F03.auth\u002F08.oauth-external-providers",{"title":834,"path":835,"stem":836},"Безпека на практиці: CORS, HTTPS та захист від атак","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fsecurity-hardening","01.csharp\u002F11.aspnet\u002F03.auth\u002F09.security-hardening",{"title":838,"path":839,"stem":840},"Теорія OAuth 2.0: Поняття, Аналогії та Флоу","\u002Fcsharp\u002Faspnet\u002Fauth\u002Foauth-theory","01.csharp\u002F11.aspnet\u002F03.auth\u002F10.oauth-theory",{"title":842,"path":843,"stem":844},"OIDC, OAuth 2.0 та Keycloak в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Foidc-keycloak","01.csharp\u002F11.aspnet\u002F03.auth\u002F10.oidc-keycloak",{"title":846,"path":847,"stem":848},"API Keys аутентифікація в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fapi-keys","01.csharp\u002F11.aspnet\u002F03.auth\u002F11.api-keys",{"title":850,"path":851,"stem":852},"Rate Limiting та Throttling в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Frate-limiting","01.csharp\u002F11.aspnet\u002F03.auth\u002F12.rate-limiting",{"title":854,"path":855,"stem":856},"Refresh Token Rotation в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Frefresh-token-rotation","01.csharp\u002F11.aspnet\u002F03.auth\u002F13.refresh-token-rotation",{"title":858,"path":859,"stem":860},"Certificate Authentication та mTLS в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fcertificate-auth","01.csharp\u002F11.aspnet\u002F03.auth\u002F14.certificate-auth",{"title":862,"path":863,"stem":864},"RBAC, ABAC та ReBAC в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Frbac-abac-rebac","01.csharp\u002F11.aspnet\u002F03.auth\u002F15.rbac-abac-rebac",{"title":866,"path":867,"stem":868},"Multi-tenancy та ізоляція даних в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fauth\u002Fmulti-tenancy","01.csharp\u002F11.aspnet\u002F03.auth\u002F16.multi-tenancy",{"title":870,"icon":871,"path":872,"stem":873,"children":874,"page":59},"Нотифікації","i-lucide-bell","\u002Fcsharp\u002Faspnet\u002Fnotifications","01.csharp\u002F11.aspnet\u002F04.notifications",[875,879,883,887,891,895,899,903,907,911,915,919,923],{"title":876,"path":877,"stem":878},"In-App нотифікації через базу даних","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fin-app-database-notifications","01.csharp\u002F11.aspnet\u002F04.notifications\u002F01.in-app-database-notifications",{"title":880,"path":881,"stem":882},"Polling: Регулярний запит оновлень","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fpolling","01.csharp\u002F11.aspnet\u002F04.notifications\u002F02.polling",{"title":884,"path":885,"stem":886},"Server-Sent Events: Однострімовий push від сервера","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fserver-sent-events","01.csharp\u002F11.aspnet\u002F04.notifications\u002F03.server-sent-events",{"title":888,"path":889,"stem":890},"WebSockets: Двостороннє з'єднання в реальному часі","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fwebsockets","01.csharp\u002F11.aspnet\u002F04.notifications\u002F04.websockets",{"title":892,"path":893,"stem":894},"SignalR: Абстракція над транспортами реального часу","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fsignalr","01.csharp\u002F11.aspnet\u002F04.notifications\u002F05.signalr",{"title":896,"path":897,"stem":898},"Background Services: Фонові задачі в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fbackground-services","01.csharp\u002F11.aspnet\u002F04.notifications\u002F06.background-services",{"title":900,"path":901,"stem":902},"Web Push нотифікації","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fweb-push","01.csharp\u002F11.aspnet\u002F04.notifications\u002F07.web-push",{"title":904,"path":905,"stem":906},"Email нотифікації","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Femail-notifications","01.csharp\u002F11.aspnet\u002F04.notifications\u002F08.email-notifications",{"title":908,"path":909,"stem":910},"Порівняння підходів: Як вибрати правильну технологію нотифікацій","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fchoosing-the-right-approach","01.csharp\u002F11.aspnet\u002F04.notifications\u002F09.choosing-the-right-approach",{"title":912,"path":913,"stem":914},"Hangfire: Надійне планування фонових задач","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fhangfire","01.csharp\u002F11.aspnet\u002F04.notifications\u002F10.hangfire",{"title":916,"path":917,"stem":918},"Практика: Конвертація зображень у WebP через Hangfire","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fhangfire-image-webp","01.csharp\u002F11.aspnet\u002F04.notifications\u002F11.hangfire-image-webp",{"title":920,"path":921,"stem":922},"Практика: Підготовка відео до HLS-стрімінгу через Hangfire","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Fhangfire-video-hls","01.csharp\u002F11.aspnet\u002F04.notifications\u002F12.hangfire-video-hls",{"title":924,"path":925,"stem":926},"Telegram-нотифікації: від одного повідомлення до масових розсилок і мульти-канального підходу","\u002Fcsharp\u002Faspnet\u002Fnotifications\u002Ftelegram-notifications","01.csharp\u002F11.aspnet\u002F04.notifications\u002F13.telegram-notifications",{"title":928,"icon":929,"path":930,"stem":931,"children":932,"page":59},"Інтернаціоналізація","i-lucide-languages","\u002Fcsharp\u002Faspnet\u002Fi18n","01.csharp\u002F11.aspnet\u002F05.i18n",[933,937],{"title":934,"path":935,"stem":936},"Інтернаціоналізація (i18n) у Minimal API: від A до Я","\u002Fcsharp\u002Faspnet\u002Fi18n\u002Finternationalization","01.csharp\u002F11.aspnet\u002F05.i18n\u002F01.internationalization",{"title":938,"path":939,"stem":940},"Humanizer: людиномовні рядки у .NET","\u002Fcsharp\u002Faspnet\u002Fi18n\u002Fhumanizer","01.csharp\u002F11.aspnet\u002F05.i18n\u002F02.humanizer",{"title":942,"icon":943,"path":944,"stem":945,"children":946,"page":59},"Кешування","i-lucide-layers","\u002Fcsharp\u002Faspnet\u002Fcaching","01.csharp\u002F11.aspnet\u002F06.caching",[947,951,955,959,963],{"title":948,"path":949,"stem":950},"Огляд кешування: чотири рівні і коли що обирати","\u002Fcsharp\u002Faspnet\u002Fcaching\u002Fcaching","01.csharp\u002F11.aspnet\u002F06.caching\u002F01.caching",{"title":952,"path":953,"stem":954},"IMemoryCache: кеш в оперативній пам'яті","\u002Fcsharp\u002Faspnet\u002Fcaching\u002Fmemory-cache","01.csharp\u002F11.aspnet\u002F06.caching\u002F02.memory-cache",{"title":956,"path":957,"stem":958},"IDistributedCache і Redis: розподілений кеш","\u002Fcsharp\u002Faspnet\u002Fcaching\u002Fdistributed-cache","01.csharp\u002F11.aspnet\u002F06.caching\u002F03.distributed-cache",{"title":960,"path":961,"stem":962},"Response Cache: HTTP-кешування через Cache-Control","\u002Fcsharp\u002Faspnet\u002Fcaching\u002Fresponse-cache","01.csharp\u002F11.aspnet\u002F06.caching\u002F04.response-cache",{"title":964,"path":965,"stem":966},"Output Cache: серверний кеш HTTP-відповідей (.NET 7+)","\u002Fcsharp\u002Faspnet\u002Fcaching\u002Foutput-cache","01.csharp\u002F11.aspnet\u002F06.caching\u002F05.output-cache",{"title":968,"icon":969,"path":970,"stem":971,"children":972,"page":59},"Тестування","i-lucide-test-tube","\u002Fcsharp\u002Faspnet\u002Ftesting","01.csharp\u002F11.aspnet\u002F07.testing",[973,977,981,985,989,993,997,1001,1005,1009,1013,1017,1021,1025,1029,1033,1037,1041,1045,1049,1053],{"title":974,"path":975,"stem":976},"Що таке тестування? Від інтуїції до науки","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fwhat-is-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F01.what-is-testing",{"title":978,"path":979,"stem":980},"Піраміда тестування — Стратегія, а не Догма","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Ftesting-pyramid","01.csharp\u002F11.aspnet\u002F07.testing\u002F02.testing-pyramid",{"title":982,"path":983,"stem":984},"Дві Школи Тестування — Лондон проти Детройту","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Ftesting-schools","01.csharp\u002F11.aspnet\u002F07.testing\u002F03.testing-schools",{"title":986,"path":987,"stem":988},"TDD та BDD — Тести як Дизайн-інструмент","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Ftdd-and-bdd","01.csharp\u002F11.aspnet\u002F07.testing\u002F04.tdd-and-bdd",{"title":990,"path":991,"stem":992},"Що саме тестувати — Техніки аналізу та Циклomatична складність","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fwhat-to-test","01.csharp\u002F11.aspnet\u002F07.testing\u002F05.what-to-test",{"title":994,"path":995,"stem":996},"Тестові Фреймворки — Навіщо вони і що всередині","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Ftest-frameworks","01.csharp\u002F11.aspnet\u002F07.testing\u002F06.test-frameworks",{"title":998,"path":999,"stem":1000},"xUnit — Факти, Теорії та Lifecycle тестів","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fxunit-basics","01.csharp\u002F11.aspnet\u002F07.testing\u002F07.xunit-basics",{"title":1002,"path":1003,"stem":1004},"xUnit Advanced — Fixtures, Кастомізація та Розширення","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fxunit-advanced","01.csharp\u002F11.aspnet\u002F07.testing\u002F08.xunit-advanced",{"title":1006,"path":1007,"stem":1008},"Moq — Глибоке занурення в мокування","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fmocking-with-moq","01.csharp\u002F11.aspnet\u002F07.testing\u002F09.mocking-with-moq",{"title":1010,"path":1011,"stem":1012},"Тестування Баз Даних — EF Core, SQLite та Testcontainers","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fdatabase-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F10.database-testing",{"title":1014,"path":1015,"stem":1016},"Integration Testing — Частина 1 [Теорія та WebApplicationFactory]","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fintegration-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F11.integration-testing",{"title":1018,"path":1019,"stem":1020},"Інтеграційне тестування — Практика","\u002Fcsharp\u002Faspnet\u002Ftesting\u002F11a.integration-testing-practice","01.csharp\u002F11.aspnet\u002F07.testing\u002F11a.integration-testing-practice",{"title":1022,"path":1023,"stem":1024},"Integration Testing — Частина 2 [Просунуті Сценарії та Testcontainers]","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fintegration-testing-advanced","01.csharp\u002F11.aspnet\u002F07.testing\u002F12.integration-testing-advanced",{"title":1026,"path":1027,"stem":1028},"Професійний Postman: Колекції, Змінні та GitHub Інтеграція","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fpostman-professional","01.csharp\u002F11.aspnet\u002F07.testing\u002F13.postman-professional",{"title":1030,"path":1031,"stem":1032},"HttpClient у Тестах Частина 1: Архітектура та MockHttpMessageHandler","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fhttpclient-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F14.httpclient-testing",{"title":1034,"path":1035,"stem":1036},"HttpClient у Тестах Частина 2: WireMock.Net та Resilience","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fwiremock-net","01.csharp\u002F11.aspnet\u002F07.testing\u002F15.wiremock-net",{"title":1038,"path":1039,"stem":1040},"Патерни та Анти-патерни Тестування: Test Smells","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Ftesting-patterns","01.csharp\u002F11.aspnet\u002F07.testing\u002F16.testing-patterns",{"title":1042,"path":1043,"stem":1044},"Просунуті інструменти: Time, Snapshots та Властивості","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fadvanced-testing-tools","01.csharp\u002F11.aspnet\u002F07.testing\u002F17.advanced-testing-tools",{"title":1046,"path":1047,"stem":1048},"Тестування Архітектури з NetArchTest","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Farchitecture-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F18.architecture-testing",{"title":1050,"path":1051,"stem":1052},"Тестування Продуктивності: BenchmarkDotNet, NBomber та k6","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fperformance-testing","01.csharp\u002F11.aspnet\u002F07.testing\u002F19.performance-testing",{"title":1054,"path":1055,"stem":1056},"Залишок плану для курсу \"Тестування ASP.NET Minimal API\"","\u002Fcsharp\u002Faspnet\u002Ftesting\u002Fremaining_plan","01.csharp\u002F11.aspnet\u002F07.testing\u002Fremaining_plan",{"title":1058,"icon":1059,"path":1060,"stem":1061,"children":1062,"page":59},"Платежі","i-lucide-credit-card","\u002Fcsharp\u002Faspnet\u002Fpayments","01.csharp\u002F11.aspnet\u002F08.payments",[1063,1067,1071,1075,1079,1083,1087,1091,1095,1099,1103,1107],{"title":1064,"path":1065,"stem":1066},"Основи платіжної інфраструктури","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fpayment-fundamentals","01.csharp\u002F11.aspnet\u002F08.payments\u002F01.payment-fundamentals",{"title":1068,"path":1069,"stem":1070},"Методи оплати в Україні","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fpayment-methods-ukraine","01.csharp\u002F11.aspnet\u002F08.payments\u002F02.payment-methods-ukraine",{"title":1072,"path":1073,"stem":1074},"PCI DSS та безпека платежів","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fpci-dss-security","01.csharp\u002F11.aspnet\u002F08.payments\u002F03.pci-dss-security",{"title":1076,"path":1077,"stem":1078},"Архітектура платіжної підсистеми","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fpayment-architecture","01.csharp\u002F11.aspnet\u002F08.payments\u002F04.payment-architecture",{"title":1080,"path":1081,"stem":1082},"Інтеграція LiqPay (ПриватБанк)","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fliqpay-integration","01.csharp\u002F11.aspnet\u002F08.payments\u002F05.liqpay-integration",{"title":1084,"path":1085,"stem":1086},"Інтеграція Monobank Acquiring API","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fmonobank-acquiring","01.csharp\u002F11.aspnet\u002F08.payments\u002F06.monobank-acquiring",{"title":1088,"path":1089,"stem":1090},"Інтеграція Stripe","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fstripe-integration","01.csharp\u002F11.aspnet\u002F08.payments\u002F07.stripe-integration",{"title":1092,"path":1093,"stem":1094},"Webhooks — глибоке занурення","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fwebhooks-deep-dive","01.csharp\u002F11.aspnet\u002F08.payments\u002F08.webhooks-deep-dive",{"title":1096,"path":1097,"stem":1098},"Підписки та рекурентні платежі","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fsubscriptions-recurring","01.csharp\u002F11.aspnet\u002F08.payments\u002F09.subscriptions-recurring",{"title":1100,"path":1101,"stem":1102},"Повернення коштів та диспути","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Frefunds-disputes","01.csharp\u002F11.aspnet\u002F08.payments\u002F10.refunds-disputes",{"title":1104,"path":1105,"stem":1106},"Тестування платіжних інтеграцій","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Ftesting-payments","01.csharp\u002F11.aspnet\u002F08.payments\u002F11.testing-payments",{"title":1108,"path":1109,"stem":1110},"Чекліст виходу в Production","\u002Fcsharp\u002Faspnet\u002Fpayments\u002Fproduction-checklist","01.csharp\u002F11.aspnet\u002F08.payments\u002F12.production-checklist",{"title":1112,"icon":1113,"items":1114,"path":1127,"stem":1128,"children":1129,"page":59},"Популярні бібліотеки","lucide:box",[1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126],"01.fluent-validation","02.mapster","03.erroror-result-pattern","04.serilog","05.mediatr","06.polly","07.health-checks","08.feature-management","09.fluent-email","10.quest-pdf","11.bogus","12.humanizer-guard","\u002Fcsharp\u002Faspnet\u002Flibraries","01.csharp\u002F11.aspnet\u002F09.libraries",[1130,1134,1138,1142,1146,1150,1154,1158,1162,1166,1170,1174,1178],{"title":1131,"path":1132,"stem":1133},"Валідація з FluentValidation в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Ffluent-validation","01.csharp\u002F11.aspnet\u002F09.libraries\u002F01.fluent-validation",{"title":1135,"path":1136,"stem":1137},"Маппінг об","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fmapster","01.csharp\u002F11.aspnet\u002F09.libraries\u002F02.mapster",{"title":1139,"path":1140,"stem":1141},"Обробка помилок з ErrorOr та Result Pattern в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Ferroror-result-pattern","01.csharp\u002F11.aspnet\u002F09.libraries\u002F03.erroror-result-pattern",{"title":1143,"path":1144,"stem":1145},"Структуроване логування з Serilog в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fserilog","01.csharp\u002F11.aspnet\u002F09.libraries\u002F04.serilog",{"title":1147,"path":1148,"stem":1149},"CQRS та Mediator з MediatR в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fmediatr","01.csharp\u002F11.aspnet\u002F09.libraries\u002F05.mediatr",{"title":1151,"path":1152,"stem":1153},"Відмовостійкість з Polly в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fpolly","01.csharp\u002F11.aspnet\u002F09.libraries\u002F06.polly",{"title":1155,"path":1156,"stem":1157},"Health Checks в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fhealth-checks","01.csharp\u002F11.aspnet\u002F09.libraries\u002F07.health-checks",{"title":1159,"path":1160,"stem":1161},"Feature Management та Feature Flags в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Ffeature-management","01.csharp\u002F11.aspnet\u002F09.libraries\u002F08.feature-management",{"title":1163,"path":1164,"stem":1165},"Відправка Email з FluentEmail в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Ffluent-email","01.csharp\u002F11.aspnet\u002F09.libraries\u002F09.fluent-email",{"title":1167,"path":1168,"stem":1169},"Генерація PDF з QuestPDF в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fquest-pdf","01.csharp\u002F11.aspnet\u002F09.libraries\u002F10.quest-pdf",{"title":1171,"path":1172,"stem":1173},"Генерація тестових даних з Bogus в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fbogus","01.csharp\u002F11.aspnet\u002F09.libraries\u002F11.bogus",{"title":1175,"path":1176,"stem":1177},"Humanizer та Guard Clauses в ASP.NET Core","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fhumanizer-guard","01.csharp\u002F11.aspnet\u002F09.libraries\u002F12.humanizer-guard",{"title":1179,"path":1180,"stem":1181},"План модуля 10.libraries — Популярні бібліотеки ASP.NET","\u002Fcsharp\u002Faspnet\u002Flibraries\u002Fplan","01.csharp\u002F11.aspnet\u002F09.libraries\u002Fplan",{"title":1183,"icon":1184,"path":1185,"stem":1186,"children":1187,"page":59},"Razor Pages","i-lucide-layout-template","\u002Fcsharp\u002Faspnet\u002Frazor-pages","01.csharp\u002F11.aspnet\u002F10.razor-pages",[1188,1192,1196,1200,1204,1208],{"title":1189,"path":1190,"stem":1191},"Від Minimal API до Razor Pages: концептуальний перехід","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Ffrom-minimal-api","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F01.from-minimal-api",{"title":1193,"path":1194,"stem":1195},"PageModel: логіка сторінки Razor Pages","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Fpage-model","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F02.page-model",{"title":1197,"path":1198,"stem":1199},"Razor синтаксис: шаблонізатор у .cshtml","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Frazor-syntax","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F03.razor-syntax",{"title":1201,"path":1202,"stem":1203},"Tag Helpers: типізований HTML","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Ftag-helpers","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F04.tag-helpers",{"title":1205,"path":1206,"stem":1207},"Форми і валідація: повний цикл обробки даних","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Fforms-validation","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F05.forms-validation",{"title":1209,"path":1210,"stem":1211},"Практичний проєкт: TaskManager на Razor Pages","\u002Fcsharp\u002Faspnet\u002Frazor-pages\u002Fproject-task-manager","01.csharp\u002F11.aspnet\u002F10.razor-pages\u002F06.project-task-manager",{"title":1213,"path":1214,"stem":1215,"children":1216,"page":59},"ASP.NET Core MVC","\u002Fcsharp\u002Faspnet\u002Fmvc","01.csharp\u002F11.aspnet\u002F11.mvc",[1217,1221,1225,1229,1233,1237,1241,1245,1249,1253,1257,1261,1265,1269,1273,1277,1281,1285],{"title":1218,"path":1219,"stem":1220},"Патерн MVC: архітектура, що змінила веб","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fmvc-pattern","01.csharp\u002F11.aspnet\u002F11.mvc\u002F01.mvc-pattern",{"title":1222,"path":1223,"stem":1224},"Від Razor Pages до MVC: концептуальний перехід","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Ffrom-razor-pages","01.csharp\u002F11.aspnet\u002F11.mvc\u002F02.from-razor-pages",{"title":1226,"path":1227,"stem":1228},"Controllers та Actions: серце MVC","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fcontrollers-actions","01.csharp\u002F11.aspnet\u002F11.mvc\u002F03.controllers-actions",{"title":1230,"path":1231,"stem":1232},"Маршрутизація в MVC: Convention vs Attribute Routing","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Frouting-mvc","01.csharp\u002F11.aspnet\u002F11.mvc\u002F04.routing-mvc",{"title":1234,"path":1235,"stem":1236},"Model Binding: від HTTP до C#","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fmodel-binding","01.csharp\u002F11.aspnet\u002F11.mvc\u002F05.model-binding",{"title":1238,"path":1239,"stem":1240},"Views, ViewData, ViewBag, TempData і ViewModel","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fviews-viewdata-tempdata","01.csharp\u002F11.aspnet\u002F11.mvc\u002F06.views-viewdata-tempdata",{"title":1242,"path":1243,"stem":1244},"Filters: аспектно-орієнтоване програмування в MVC","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Ffilters","01.csharp\u002F11.aspnet\u002F11.mvc\u002F07.filters",{"title":1246,"path":1247,"stem":1248},"Areas: структурування великих застосунків","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fareas","01.csharp\u002F11.aspnet\u002F11.mvc\u002F08.areas",{"title":1250,"path":1251,"stem":1252},"View Components: повторювані незалежні блоки UI","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fview-components","01.csharp\u002F11.aspnet\u002F11.mvc\u002F09.view-components",{"title":1254,"path":1255,"stem":1256},"Display та Editor Templates","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fdisplay-editor-templates","01.csharp\u002F11.aspnet\u002F11.mvc\u002F10.display-editor-templates",{"title":1258,"path":1259,"stem":1260},"Валідація: IValidatableObject та FluentValidation","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fvalidation-advanced","01.csharp\u002F11.aspnet\u002F11.mvc\u002F11.validation-advanced",{"title":1262,"path":1263,"stem":1264},"HTMX: інтерактивність через HTML-атрибути","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fhtmx","01.csharp\u002F11.aspnet\u002F11.mvc\u002F12.htmx",{"title":1266,"path":1267,"stem":1268},"HTMX у ASP.NET Core MVC: серверна інтеграція","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fajax-htmx-mvc","01.csharp\u002F11.aspnet\u002F11.mvc\u002F13.ajax-htmx-mvc",{"title":1270,"path":1271,"stem":1272},"Практичний проєкт: Каталог товарів з HTMX","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fhtmx-project","01.csharp\u002F11.aspnet\u002F11.mvc\u002F14.htmx-project",{"title":1274,"path":1275,"stem":1276},"Завантаження та обробка файлів","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Ffile-upload","01.csharp\u002F11.aspnet\u002F11.mvc\u002F15.file-upload",{"title":1278,"path":1279,"stem":1280},"Глобалізація та Локалізація MVC","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fglobalization-localization","01.csharp\u002F11.aspnet\u002F11.mvc\u002F16.globalization-localization",{"title":1282,"path":1283,"stem":1284},"Підсумковий проєкт: Блог-платформа","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fmvc-project","01.csharp\u002F11.aspnet\u002F11.mvc\u002F17.mvc-project",{"title":1286,"path":1287,"stem":1288},"План курсу: ASP.NET Core MVC","\u002Fcsharp\u002Faspnet\u002Fmvc\u002Fplan","01.csharp\u002F11.aspnet\u002F11.mvc\u002Fplan",{"title":1290,"path":1291,"stem":1292,"children":1293,"page":59},"Web Api","\u002Fcsharp\u002Faspnet\u002Fweb-api","01.csharp\u002F11.aspnet\u002F12.web-api",[1294,1298,1302,1306,1310,1314,1318,1322,1326,1330,1334,1338,1342],{"title":1295,"path":1296,"stem":1297},"Від Minimal API до Controller-based API","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Ffrom-minimal-api-to-controllers","01.csharp\u002F11.aspnet\u002F12.web-api\u002F01.from-minimal-api-to-controllers",{"title":1299,"path":1300,"stem":1301},"ControllerBase, ActionResult\u003CT> та Response Types","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fcontroller-base-actionresult","01.csharp\u002F11.aspnet\u002F12.web-api\u002F02.controller-base-actionresult",{"title":1303,"path":1304,"stem":1305},"Content Negotiation - JSON, XML та власні форматери","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fcontent-negotiation","01.csharp\u002F11.aspnet\u002F12.web-api\u002F03.content-negotiation",{"title":1307,"path":1308,"stem":1309},"Версіонування API","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fapi-versioning","01.csharp\u002F11.aspnet\u002F12.web-api\u002F04.api-versioning",{"title":1311,"path":1312,"stem":1313},"ProblemDetails та структурована обробка помилок","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fproblemdetails-error-handling","01.csharp\u002F11.aspnet\u002F12.web-api\u002F05.problemdetails-error-handling",{"title":1315,"path":1316,"stem":1317},"Фільтри у Web API контексті","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Ffilters-for-api","01.csharp\u002F11.aspnet\u002F12.web-api\u002F06.filters-for-api",{"title":1319,"path":1320,"stem":1321},"Пагінація, фільтрація та сортування","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fpagination-filtering-sorting","01.csharp\u002F11.aspnet\u002F12.web-api\u002F07.pagination-filtering-sorting",{"title":1323,"path":1324,"stem":1325},"HATEOAS та Resource Expansion","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fhateoas-resource-expansion","01.csharp\u002F11.aspnet\u002F12.web-api\u002F08.hateoas-resource-expansion",{"title":1327,"path":1328,"stem":1329},"Гібридна архітектура - Minimal API + Controllers","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fminimal-api-vs-controllers-hybrid","01.csharp\u002F11.aspnet\u002F12.web-api\u002F09.minimal-api-vs-controllers-hybrid",{"title":1331,"path":1332,"stem":1333},"Документація API - Swashbuckle, NSwag та генерація клієнтів","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fapi-documentation-generation","01.csharp\u002F11.aspnet\u002F12.web-api\u002F10.api-documentation-generation",{"title":1335,"path":1336,"stem":1337},"Health Checks та моніторинг API","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fhealth-checks-monitoring","01.csharp\u002F11.aspnet\u002F12.web-api\u002F11.health-checks-monitoring",{"title":1339,"path":1340,"stem":1341},"Підсумковий проєкт - Production-Ready REST API","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fweb-api-project","01.csharp\u002F11.aspnet\u002F12.web-api\u002F12.web-api-project",{"title":1343,"path":1344,"stem":1345},"План курсу: ASP.NET Core Web API (Controllers)","\u002Fcsharp\u002Faspnet\u002Fweb-api\u002Fplan","01.csharp\u002F11.aspnet\u002F12.web-api\u002Fplan",{"title":1347,"icon":1348,"path":1349,"stem":1350,"children":1351,"page":59},"Моніторинг","i-lucide-activity","\u002Fcsharp\u002Faspnet\u002Fmonitoring","01.csharp\u002F11.aspnet\u002F13.monitoring",[1352,1356,1360],{"title":1353,"path":1354,"stem":1355},"Спостережуваність: від console.log до production-систем","\u002Fcsharp\u002Faspnet\u002Fmonitoring\u002Fobservability-intro","01.csharp\u002F11.aspnet\u002F13.monitoring\u002F01.observability-intro",{"title":1357,"path":1358,"stem":1359},"Health Checks: перший рівень observability","\u002Fcsharp\u002Faspnet\u002Fmonitoring\u002Fhealth-checks","01.csharp\u002F11.aspnet\u002F13.monitoring\u002F02.health-checks",{"title":1361,"path":1362,"stem":1363},"Вбудовані метрики .NET 10 та System.Diagnostics.Metrics","\u002Fcsharp\u002Faspnet\u002Fmonitoring\u002Fdotnet-metrics","01.csharp\u002F11.aspnet\u002F13.monitoring\u002F03.dotnet-metrics",{"title":1365,"icon":1366,"path":1367,"stem":1368,"children":1369,"page":59},"Desktop UI","i-lucide-app-window","\u002Fcsharp\u002Fdesktop-ui","01.csharp\u002F12.desktop-ui",[1370,1374,1378,1382,1386,1390,1394,1398,1402,1406,1410,1414,1418,1422,1426,1430,1434,1438,1442,1446,1450,1454,1458,1462,1466,1470,1474,1478,1482,1486,1490,1494,1498,1502,1506,1510,1514,1518,1522,1526,1530,1534,1538,1542,1546,1550,1554,1558,1562,1566,1570,1574,1578,1582,1586,1590,1594,1598,1602,1606,1610,1614,1618,1622,1626,1630,1634,1638,1642,1646,1650],{"title":1371,"path":1372,"stem":1373},"Що таке десктопна розробка?","\u002Fcsharp\u002Fdesktop-ui\u002Fwhat-is-desktop-dev","01.csharp\u002F12.desktop-ui\u002F01.what-is-desktop-dev",{"title":1375,"path":1376,"stem":1377},"Архітектура WPF — як влаштований графічний інтерфейс","\u002Fcsharp\u002Fdesktop-ui\u002Fwpf-architecture","01.csharp\u002F12.desktop-ui\u002F02.wpf-architecture",{"title":1379,"path":1380,"stem":1381},"Перший WPF-проєкт — від нуля до вікна","\u002Fcsharp\u002Fdesktop-ui\u002Ffirst-wpf-app","01.csharp\u002F12.desktop-ui\u002F03.first-wpf-app",{"title":1383,"path":1384,"stem":1385},"Перший Avalonia-проєкт: WPF для всіх платформ","\u002Fcsharp\u002Fdesktop-ui\u002F03a.first-avalonia-app","01.csharp\u002F12.desktop-ui\u002F03a.first-avalonia-app",{"title":1387,"path":1388,"stem":1389},"XAML: декларативний інтерфейс","\u002Fcsharp\u002Fdesktop-ui\u002Fxaml-basics","01.csharp\u002F12.desktop-ui\u002F04.xaml-basics",{"title":1391,"path":1392,"stem":1393},"Fluent UI у WPF — сучасний дизайн Windows 11","\u002Fcsharp\u002Fdesktop-ui\u002F04a.wpf-fluent-ui","01.csharp\u002F12.desktop-ui\u002F04a.wpf-fluent-ui",{"title":1395,"path":1396,"stem":1397},"WPF UI — сучасна бібліотека Fluent контролів","\u002Fcsharp\u002Fdesktop-ui\u002F04b.wpf-ui-library","01.csharp\u002F12.desktop-ui\u002F04b.wpf-ui-library",{"title":1399,"path":1400,"stem":1401},"HandyControl — велика бібліотека UI контролів для WPF","\u002Fcsharp\u002Fdesktop-ui\u002F04c.handycontrol-library","01.csharp\u002F12.desktop-ui\u002F04c.handycontrol-library",{"title":1403,"path":1404,"stem":1405},"Простори імен та ресурси XAML","\u002Fcsharp\u002Fdesktop-ui\u002Fxaml-namespaces-resources","01.csharp\u002F12.desktop-ui\u002F05.xaml-namespaces-resources",{"title":1407,"path":1408,"stem":1409},"XAML в Avalonia: ключові відмінності від WPF","\u002Fcsharp\u002Fdesktop-ui\u002F05a.avalonia-xaml-differences","01.csharp\u002F12.desktop-ui\u002F05a.avalonia-xaml-differences",{"title":1411,"path":1412,"stem":1413},"Розширення розмітки XAML (Markup Extensions)","\u002Fcsharp\u002Fdesktop-ui\u002Fxaml-markup-extensions","01.csharp\u002F12.desktop-ui\u002F06.xaml-markup-extensions",{"title":1415,"path":1416,"stem":1417},"Панелі Layout: StackPanel, WrapPanel, DockPanel","\u002Fcsharp\u002Fdesktop-ui\u002Flayout-panels-part1","01.csharp\u002F12.desktop-ui\u002F07.layout-panels-part1",{"title":1419,"path":1420,"stem":1421},"Grid, Canvas, UniformGrid","\u002Fcsharp\u002Fdesktop-ui\u002Flayout-panels-part2","01.csharp\u002F12.desktop-ui\u002F07.layout-panels-part2",{"title":1423,"path":1424,"stem":1425},"Просунуті техніки Layout","\u002Fcsharp\u002Fdesktop-ui\u002Flayout-advanced","01.csharp\u002F12.desktop-ui\u002F08.layout-advanced",{"title":1427,"path":1428,"stem":1429},"Адаптивний Layout та найкращі практики","\u002Fcsharp\u002Fdesktop-ui\u002Flayout-responsive","01.csharp\u002F12.desktop-ui\u002F09.layout-responsive",{"title":1431,"path":1432,"stem":1433},"Layout в Avalonia: відмінності та нові можливості","\u002Fcsharp\u002Fdesktop-ui\u002F09a.layout-avalonia","01.csharp\u002F12.desktop-ui\u002F09a.layout-avalonia",{"title":1435,"path":1436,"stem":1437},"Button, Image, ProgressBar та інші базові контроли","\u002Fcsharp\u002Fdesktop-ui\u002Fbasic-controls","01.csharp\u002F12.desktop-ui\u002F10.basic-controls",{"title":1439,"path":1440,"stem":1441},"Контроли в Avalonia: відмінності від WPF","\u002Fcsharp\u002Fdesktop-ui\u002F10a.controls-avalonia","01.csharp\u002F12.desktop-ui\u002F10a.controls-avalonia",{"title":1443,"path":1444,"stem":1445},"Текстові контроли — TextBlock, TextBox, RichTextBox","\u002Fcsharp\u002Fdesktop-ui\u002Ftext-controls","01.csharp\u002F12.desktop-ui\u002F11.text-controls",{"title":1447,"path":1448,"stem":1449},"Контроли вибору — CheckBox, RadioButton, ComboBox, ListBox, DatePicker","\u002Fcsharp\u002Fdesktop-ui\u002Fselection-controls","01.csharp\u002F12.desktop-ui\u002F12.selection-controls",{"title":1451,"path":1452,"stem":1453},"Content Model — GroupBox, Expander, TabControl, StatusBar","\u002Fcsharp\u002Fdesktop-ui\u002Fcontent-controls","01.csharp\u002F12.desktop-ui\u002F13.content-controls",{"title":1455,"path":1456,"stem":1457},"UI\u002FUX принципи десктопних застосунків","\u002Fcsharp\u002Fdesktop-ui\u002F13a.ui-ux-principles","01.csharp\u002F12.desktop-ui\u002F13a.ui-ux-principles",{"title":1459,"path":1460,"stem":1461},"Dependency Properties — Концепція та Value Resolution","\u002Fcsharp\u002Fdesktop-ui\u002Fdependency-properties-part1","01.csharp\u002F12.desktop-ui\u002F14.dependency-properties-part1",{"title":1463,"path":1464,"stem":1465},"Avalonia Property System — StyledProperty та DirectProperty","\u002Fcsharp\u002Fdesktop-ui\u002F14a.avalonia-property-system","01.csharp\u002F12.desktop-ui\u002F14a.avalonia-property-system",{"title":1467,"path":1468,"stem":1469},"Attached Properties — Властивості без меж","\u002Fcsharp\u002Fdesktop-ui\u002Fattached-properties","01.csharp\u002F12.desktop-ui\u002F15.attached-properties",{"title":1471,"path":1472,"stem":1473},"Routed Events — Маршрутизація подій у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Frouted-events","01.csharp\u002F12.desktop-ui\u002F16.routed-events",{"title":1475,"path":1476,"stem":1477},"Data Binding — Від Code-Behind до Декларативності","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-binding-basics-part1","01.csharp\u002F12.desktop-ui\u002F17.data-binding-basics-part1",{"title":1479,"path":1480,"stem":1481},"INotifyPropertyChanged — Живе оновлення UI","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-binding-basics-part2","01.csharp\u002F12.desktop-ui\u002F17.data-binding-basics-part2",{"title":1483,"path":1484,"stem":1485},"Compiled Bindings в Avalonia — Безпека на етапі компіляції","\u002Fcsharp\u002Fdesktop-ui\u002F17a.avalonia-compiled-bindings","01.csharp\u002F12.desktop-ui\u002F17a.avalonia-compiled-bindings",{"title":1487,"path":1488,"stem":1489},"Просунутий Data Binding — ElementName, RelativeSource, MultiBinding","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-binding-advanced","01.csharp\u002F12.desktop-ui\u002F18.data-binding-advanced",{"title":1491,"path":1492,"stem":1493},"Value Converters — Перетворення типів даних у Data Binding","\u002Fcsharp\u002Fdesktop-ui\u002Fvalue-converters","01.csharp\u002F12.desktop-ui\u002F19.value-converters",{"title":1495,"path":1496,"stem":1497},"Data Templates — Візуалізація об'єктів у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-templates","01.csharp\u002F12.desktop-ui\u002F20.data-templates",{"title":1499,"path":1500,"stem":1501},"Collections Binding Part 1 — ObservableCollection та ItemsControl","\u002Fcsharp\u002Fdesktop-ui\u002Fcollections-binding-part1","01.csharp\u002F12.desktop-ui\u002F21.collections-binding-part1",{"title":1503,"path":1504,"stem":1505},"Collections Binding Part 2 — ICollectionView, Filtering, Sorting та Virtualization","\u002Fcsharp\u002Fdesktop-ui\u002Fcollections-binding-part2","01.csharp\u002F12.desktop-ui\u002F21.collections-binding-part2",{"title":1507,"path":1508,"stem":1509},"MVVM Pattern — Від Spaghetti Code до архітектури","\u002Fcsharp\u002Fdesktop-ui\u002Fmvvm-pattern","01.csharp\u002F12.desktop-ui\u002F22.mvvm-pattern",{"title":1511,"path":1512,"stem":1513},"ViewModel Implementation — Від BaseViewModel до валідації","\u002Fcsharp\u002Fdesktop-ui\u002Fviewmodel-implementation","01.csharp\u002F12.desktop-ui\u002F23.viewmodel-implementation",{"title":1515,"path":1516,"stem":1517},"Commands — Від event handlers до декларативних команд","\u002Fcsharp\u002Fdesktop-ui\u002Fcommands","01.csharp\u002F12.desktop-ui\u002F24.commands",{"title":1519,"path":1520,"stem":1521},"MVVM Toolkit — MVVM без boilerplate через Source Generators","\u002Fcsharp\u002Fdesktop-ui\u002Fmvvm-toolkit","01.csharp\u002F12.desktop-ui\u002F25.mvvm-toolkit",{"title":1523,"path":1524,"stem":1525},"Messenger Pattern — Комунікація між ViewModel без прямих посилань","\u002Fcsharp\u002Fdesktop-ui\u002Fmessenger-pattern","01.csharp\u002F12.desktop-ui\u002F26.messenger-pattern",{"title":1527,"path":1528,"stem":1529},"Стилі WPF — CSS для десктопу","\u002Fcsharp\u002Fdesktop-ui\u002Fstyles-basics","01.csharp\u002F12.desktop-ui\u002F27.styles-basics",{"title":1531,"path":1532,"stem":1533},"CSS-like стилі Avalonia","\u002Fcsharp\u002Fdesktop-ui\u002F27a.avalonia-css-styling","01.csharp\u002F12.desktop-ui\u002F27a.avalonia-css-styling",{"title":1535,"path":1536,"stem":1537},"Control Templates — Частина 1. Концепція та TemplateBinding","\u002Fcsharp\u002Fdesktop-ui\u002Fcontrol-templates-part1","01.csharp\u002F12.desktop-ui\u002F28.control-templates-part1",{"title":1539,"path":1540,"stem":1541},"Control Templates — Частина 2. Named Parts та ContentPresenter","\u002Fcsharp\u002Fdesktop-ui\u002Fcontrol-templates-part2","01.csharp\u002F12.desktop-ui\u002F28.control-templates-part2",{"title":1543,"path":1544,"stem":1545},"Control Themes в Avalonia — нова ера стилізації","\u002Fcsharp\u002Fdesktop-ui\u002F28a.avalonia-control-themes","01.csharp\u002F12.desktop-ui\u002F28a.avalonia-control-themes",{"title":1547,"path":1548,"stem":1549},"Triggers та Visual State Manager у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Ftriggers-visual-states","01.csharp\u002F12.desktop-ui\u002F29.triggers-visual-states",{"title":1551,"path":1552,"stem":1553},"Pseudo-classes в Avalonia — замість WPF Triggers","\u002Fcsharp\u002Fdesktop-ui\u002F29a.avalonia-pseudo-classes","01.csharp\u002F12.desktop-ui\u002F29a.avalonia-pseudo-classes",{"title":1555,"path":1556,"stem":1557},"Теми та ресурсні словники у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fresources-themes","01.csharp\u002F12.desktop-ui\u002F30.resources-themes",{"title":1559,"path":1560,"stem":1561},"Avalonia Themes — Fluent Design та система тематизації","\u002Fcsharp\u002Fdesktop-ui\u002F30a.avalonia-themes-fluent","01.csharp\u002F12.desktop-ui\u002F30a.avalonia-themes-fluent",{"title":1563,"path":1564,"stem":1565},"Контроли колекцій — глибоке занурення","\u002Fcsharp\u002Fdesktop-ui\u002Fcollection-controls","01.csharp\u002F12.desktop-ui\u002F31.collection-controls",{"title":1567,"path":1568,"stem":1569},"DataGrid — колонки та базове відображення","\u002Fcsharp\u002Fdesktop-ui\u002Fdatagrid-part1","01.csharp\u002F12.desktop-ui\u002F32.datagrid-part1",{"title":1571,"path":1572,"stem":1573},"DataGrid — сортування, фільтрація, редагування","\u002Fcsharp\u002Fdesktop-ui\u002Fdatagrid-part2","01.csharp\u002F12.desktop-ui\u002F32.datagrid-part2",{"title":1575,"path":1576,"stem":1577},"TreeView та GridView","\u002Fcsharp\u002Fdesktop-ui\u002Ftreeview-listview","01.csharp\u002F12.desktop-ui\u002F33.treeview-listview",{"title":1579,"path":1580,"stem":1581},"Меню, Toolbar, ContextMenu, StatusBar","\u002Fcsharp\u002Fdesktop-ui\u002Fmenus-toolbars","01.csharp\u002F12.desktop-ui\u002F34.menus-toolbars",{"title":1583,"path":1584,"stem":1585},"Навігація та керування вікнами. Частина 1: вікна та сторінки","\u002Fcsharp\u002Fdesktop-ui\u002Fnavigation-windows-part1","01.csharp\u002F12.desktop-ui\u002F35.navigation-windows-part1",{"title":1587,"path":1588,"stem":1589},"Навігація та керування вікнами. Частина 2: MVVM-навігація","\u002Fcsharp\u002Fdesktop-ui\u002Fnavigation-windows-part2","01.csharp\u002F12.desktop-ui\u002F35.navigation-windows-part2",{"title":1591,"path":1592,"stem":1593},"Avalonia — Навігація та діалоги","\u002Fcsharp\u002Fdesktop-ui\u002F35a.avalonia-navigation-dialogs","01.csharp\u002F12.desktop-ui\u002F35a.avalonia-navigation-dialogs",{"title":1595,"path":1596,"stem":1597},"Діалоги та File Pickers у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fdialogs-file-pickers","01.csharp\u002F12.desktop-ui\u002F36.dialogs-file-pickers",{"title":1599,"path":1600,"stem":1601},"UserControl: компонентний підхід у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fuser-controls","01.csharp\u002F12.desktop-ui\u002F37.user-controls",{"title":1603,"path":1604,"stem":1605},"Custom Controls: Lookless Controls у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fcustom-controls","01.csharp\u002F12.desktop-ui\u002F38.custom-controls",{"title":1607,"path":1608,"stem":1609},"Avalonia TemplatedControl — Lookless Controls","\u002Fcsharp\u002Fdesktop-ui\u002F38a.avalonia-templated-controls","01.csharp\u002F12.desktop-ui\u002F38a.avalonia-templated-controls",{"title":1611,"path":1612,"stem":1613},"Анімації у WPF: Storyboard та Easing Functions","\u002Fcsharp\u002Fdesktop-ui\u002Fanimations-transitions","01.csharp\u002F12.desktop-ui\u002F39.animations-transitions",{"title":1615,"path":1616,"stem":1617},"Анімації в Avalonia","\u002Fcsharp\u002Fdesktop-ui\u002F39a.avalonia-animations","01.csharp\u002F12.desktop-ui\u002F39a.avalonia-animations",{"title":1619,"path":1620,"stem":1621},"2D Графіка та Мультимедіа у WPF","\u002Fcsharp\u002Fdesktop-ui\u002Fmedia-graphics","01.csharp\u002F12.desktop-ui\u002F40.media-graphics",{"title":1623,"path":1624,"stem":1625},"Dependency Injection у WPF та Avalonia","\u002Fcsharp\u002Fdesktop-ui\u002Fdi-integration","01.csharp\u002F12.desktop-ui\u002F41.di-integration",{"title":1627,"path":1628,"stem":1629},"SQLite та EF Core у десктопних додатках","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-persistence-part1","01.csharp\u002F12.desktop-ui\u002F42.data-persistence-part1",{"title":1631,"path":1632,"stem":1633},"Repository Pattern та Unit of Work","\u002Fcsharp\u002Fdesktop-ui\u002Fdata-persistence-part2","01.csharp\u002F12.desktop-ui\u002F43.data-persistence-part2",{"title":1635,"path":1636,"stem":1637},"Тестування ViewModels","\u002Fcsharp\u002Fdesktop-ui\u002Fviewmodel-testing","01.csharp\u002F12.desktop-ui\u002F44.viewmodel-testing",{"title":1639,"path":1640,"stem":1641},"Avalonia Headless Testing — тестування UI без вікон","\u002Fcsharp\u002Fdesktop-ui\u002F44a.avalonia-headless-testing","01.csharp\u002F12.desktop-ui\u002F44a.avalonia-headless-testing",{"title":1643,"path":1644,"stem":1645},"Кросплатформна розробка з Avalonia","\u002Fcsharp\u002Fdesktop-ui\u002Favalonia-cross-platform","01.csharp\u002F12.desktop-ui\u002F45.avalonia-cross-platform",{"title":1647,"path":1648,"stem":1649},"Пакування та розгортання Avalonia додатків","\u002Fcsharp\u002Fdesktop-ui\u002Favalonia-packaging-deployment","01.csharp\u002F12.desktop-ui\u002F46.avalonia-packaging-deployment",{"title":1651,"path":1652,"stem":1653},"Розгортання WPF застосунків","\u002Fcsharp\u002Fdesktop-ui\u002Fwpf-packaging-deployment","01.csharp\u002F12.desktop-ui\u002F47.wpf-packaging-deployment",{"title":1655,"icon":658,"path":1656,"stem":1657,"children":1658,"page":59},"Network Programming","\u002Fcsharp\u002Fnetwork-programming","01.csharp\u002F13.network-programming",[1659,1663,1667,1671,1675,1679,1683,1687,1691,1695,1699],{"title":1660,"path":1661,"stem":1662},"Основи комп'ютерних мереж","\u002Fcsharp\u002Fnetwork-programming\u002Ffoundations","01.csharp\u002F13.network-programming\u002F01.foundations",{"title":1664,"path":1665,"stem":1666},"Модель OSI та стек TCP\u002FIP","\u002Fcsharp\u002Fnetwork-programming\u002Fosi-model","01.csharp\u002F13.network-programming\u002F02.osi-model",{"title":1668,"path":1669,"stem":1670},"IP-протокол та адресація","\u002Fcsharp\u002Fnetwork-programming\u002Fip-addressing","01.csharp\u002F13.network-programming\u002F03.ip-addressing",{"title":1672,"path":1673,"stem":1674},"UDP — протокол без з'єднання","\u002Fcsharp\u002Fnetwork-programming\u002Fudp","01.csharp\u002F13.network-programming\u002F05.udp",{"title":1676,"path":1677,"stem":1678},"UDP Broadcast та Multicast","\u002Fcsharp\u002Fnetwork-programming\u002Fudp-broadcast-multicast","01.csharp\u002F13.network-programming\u002F06.udp-broadcast-multicast",{"title":1680,"path":1681,"stem":1682},"HTTP — протокол вебу","\u002Fcsharp\u002Fnetwork-programming\u002Fhttp-fundamentals","01.csharp\u002F13.network-programming\u002F07.http-fundamentals",{"title":1684,"path":1685,"stem":1686},"HttpListener — вбудований HTTP-сервер .NET","\u002Fcsharp\u002Fnetwork-programming\u002F07a.http-listener","01.csharp\u002F13.network-programming\u002F07a.http-listener",{"title":1688,"path":1689,"stem":1690},"HTTP Advanced — cookies, аутентифікація та HTTPS","\u002Fcsharp\u002Fnetwork-programming\u002Fhttp-advanced","01.csharp\u002F13.network-programming\u002F08.http-advanced",{"title":1692,"path":1693,"stem":1694},"SMTP та протоколи електронної пошти","\u002Fcsharp\u002Fnetwork-programming\u002Fsmtp","01.csharp\u002F13.network-programming\u002F09.smtp",{"title":1696,"path":1697,"stem":1698},"WebSocket — повнодуплексний протокол реального часу","\u002Fcsharp\u002Fnetwork-programming\u002Fwebsockets","01.csharp\u002F13.network-programming\u002F10.websockets",{"title":1700,"path":1701,"stem":1702},"TLS\u002FSSL — криптографічний захист мережевих з'єднань","\u002Fcsharp\u002Fnetwork-programming\u002Ftls-ssl","01.csharp\u002F13.network-programming\u002F11.tls-ssl",{"title":1704,"path":1705,"stem":1706},"C# & .NET: The Ultimate Roadmap","\u002Fcsharp\u002Froadmap","01.csharp\u002Froadmap",{"title":1708,"icon":1709,"path":1710,"stem":1711,"children":1712,"page":59},"C++","i-devicon-cplusplus","\u002Fcpp","02.cpp",[1713,1717,1721,1725,1729,1733,1737,1741,1745,1748,1752,1756,1760,1764,1768,1772,1776,1780,1784,1788,1792,1796,1800,1804,1808,1812,1816,1820,1824,1828,1832,1836,1840,1844,1848,1852,1856,1860,1864,1868,1872,1876,1880,1884,1888,1892,1896,1900,1904,1908,1912,1916,1920,1924,1928,1932,1936,1940,1944,1948,1952,1956,1960,1964,1968,1972,1976,1980,1984,1988,1992,1996,2000,2004,2008,2012,2016,2020,2024,2028,2032,2036,2040,2044,2048,2052,2056,2060,2064,2068,2072,2076,2080],{"title":1714,"path":1715,"stem":1716},"Вступ у програмування та алгоритми","\u002Fcpp\u002Fintro-algorithms","02.cpp\u002F01.intro-algorithms",{"title":1718,"path":1719,"stem":1720},"Code Style: угоди про оформлення коду","\u002Fcpp\u002Fcode-style","02.cpp\u002F02.code-style",{"title":1722,"path":1723,"stem":1724},"Середовище розробки та перший проєкт","\u002Fcpp\u002Fide-setup","02.cpp\u002F03.ide-setup",{"title":1726,"path":1727,"stem":1728},"Вивід даних на екран","\u002Fcpp\u002Fdata-output","02.cpp\u002F04.data-output",{"title":1730,"path":1731,"stem":1732},"Типи даних, змінні та константи","\u002Fcpp\u002Fdata-types-variables","02.cpp\u002F05.data-types-variables",{"title":1734,"path":1735,"stem":1736},"Ввід даних з клавіатури","\u002Fcpp\u002Fdata-input","02.cpp\u002F06.data-input",{"title":1738,"path":1739,"stem":1740},"Оператори, перетворення типів та логічні операції","\u002Fcpp\u002Foperators-type-conversion","02.cpp\u002F07.operators-type-conversion",{"title":1742,"path":1743,"stem":1744},"Цикли","\u002Fcpp\u002Floops","02.cpp\u002F08.loops",{"title":32,"path":1746,"stem":1747},"\u002Fcpp\u002Farrays","02.cpp\u002F09.arrays",{"title":1749,"path":1750,"stem":1751},"Алгоритми сортування та аналіз складності","\u002Fcpp\u002Fsorting","02.cpp\u002F10.sorting",{"title":1753,"path":1754,"stem":1755},"Алгоритми пошуку","\u002Fcpp\u002Fsearching","02.cpp\u002F11.searching",{"title":1757,"path":1758,"stem":1759},"Функції: основи","\u002Fcpp\u002Ffunctions-basics","02.cpp\u002F12.functions-basics",{"title":1761,"path":1762,"stem":1763},"Функції: прототипи, область видимості та додаткові можливості","\u002Fcpp\u002Ffunctions-scope","02.cpp\u002F13.functions-scope",{"title":1765,"path":1766,"stem":1767},"Функції: перевантаження та шаблони","\u002Fcpp\u002Ffunctions-overloading-templates","02.cpp\u002F14.functions-overloading-templates",{"title":1769,"path":1770,"stem":1771},"Вказівники: основи","\u002Fcpp\u002Fpointers-basics","02.cpp\u002F15.pointers-basics",{"title":1773,"path":1774,"stem":1775},"Посилання (References)","\u002Fcpp\u002Freferences","02.cpp\u002F16.references",{"title":1777,"path":1778,"stem":1779},"Вказівники, const і масиви","\u002Fcpp\u002Fpointers-const-arrays","02.cpp\u002F17.pointers-const-arrays",{"title":1781,"path":1782,"stem":1783},"Адресна арифметика","\u002Fcpp\u002Fpointer-arithmetic","02.cpp\u002F18.pointer-arithmetic",{"title":1785,"path":1786,"stem":1787},"Динамічна пам'ять","\u002Fcpp\u002Fdynamic-memory","02.cpp\u002F19.dynamic-memory",{"title":1789,"path":1790,"stem":1791},"Вказівники типу void","\u002Fcpp\u002Fvoid-pointers","02.cpp\u002F20.void-pointers",{"title":1793,"path":1794,"stem":1795},"Вказівники на вказівники","\u002Fcpp\u002Fpointers-to-pointers","02.cpp\u002F21.pointers-to-pointers",{"title":1797,"path":1798,"stem":1799},"Оператор доступу до членів через вказівник (->)","\u002Fcpp\u002Fmember-access-operator","02.cpp\u002F22.member-access-operator",{"title":1801,"path":1802,"stem":1803},"Цикл for-each (Range-based for)","\u002Fcpp\u002Fforeach-loop","02.cpp\u002F23.foreach-loop",{"title":1805,"path":1806,"stem":1807},"Вказівники на функції","\u002Fcpp\u002Ffunction-pointers","02.cpp\u002F24.function-pointers",{"title":1809,"path":1810,"stem":1811},"Лямбда-вирази","\u002Fcpp\u002Flambdas","02.cpp\u002F25.lambdas",{"title":1813,"path":1814,"stem":1815},"Лямбда-захоплення","\u002Fcpp\u002Flambda-captures","02.cpp\u002F26.lambda-captures",{"title":1817,"path":1818,"stem":1819},"Еліпсис","\u002Fcpp\u002Fellipsis","02.cpp\u002F27.ellipsis",{"title":1821,"path":1822,"stem":1823},"Безпечні альтернативи еліпсису","\u002Fcpp\u002F27a.ellipsis","02.cpp\u002F27a.ellipsis",{"title":1825,"path":1826,"stem":1827},"Аргументи командного рядка","\u002Fcpp\u002Fcommand-line-arguments","02.cpp\u002F28.command-line-arguments",{"title":1829,"path":1830,"stem":1831},"Перерахування (enum)","\u002Fcpp\u002Fenum","02.cpp\u002F29.enum",{"title":1833,"path":1834,"stem":1835},"Класи-перерахування (enum class)","\u002Fcpp\u002Fenum-class","02.cpp\u002F30.enum-class",{"title":1837,"path":1838,"stem":1839},"Псевдоніми типів (typedef і using)","\u002Fcpp\u002Ftype-aliases","02.cpp\u002F31.type-aliases",{"title":1841,"path":1842,"stem":1843},"Системи числення та двійкова арифметика","\u002Fcpp\u002Fnumber-systems","02.cpp\u002F32.number-systems",{"title":1845,"path":1846,"stem":1847},"Структури (struct): агрегування даних","\u002Fcpp\u002Fstruct","02.cpp\u002F33.struct",{"title":1849,"path":1850,"stem":1851},"Структури у функціях","\u002Fcpp\u002Fstruct-functions","02.cpp\u002F34.struct-functions",{"title":1853,"path":1854,"stem":1855},"Масиви структур і вкладені структури","\u002Fcpp\u002Fstruct-arrays","02.cpp\u002F35.struct-arrays",{"title":1857,"path":1858,"stem":1859},"Патерни struct та межі застосування","\u002Fcpp\u002Fstruct-patterns","02.cpp\u002F36.struct-patterns",{"title":1861,"path":1862,"stem":1863},"Символи та таблиця ASCII","\u002Fcpp\u002Fascii-characters","02.cpp\u002F37.ascii-characters",{"title":1865,"path":1866,"stem":1867},"Unicode та кодування UTF","\u002Fcpp\u002Funicode-utf","02.cpp\u002F38.unicode-utf",{"title":1869,"path":1870,"stem":1871},"C-style рядки","\u002Fcpp\u002Fc-strings","02.cpp\u002F39.c-strings",{"title":1873,"path":1874,"stem":1875},"Вступ до std::string","\u002Fcpp\u002Fstd-string-intro","02.cpp\u002F40.std-string-intro",{"title":1877,"path":1878,"stem":1879},"Довжина, ємність та доступ до символів std::string","\u002Fcpp\u002Fstd-string-capacity-access","02.cpp\u002F41.std-string-capacity-access",{"title":1881,"path":1882,"stem":1883},"Модифікація std::string: присвоювання, додавання, вставка, видалення та заміна","\u002Fcpp\u002Fstd-string-modification","02.cpp\u002F42.std-string-modification",{"title":1885,"path":1886,"stem":1887},"Пошук у std::string: find, npos та практичні патерни","\u002Fcpp\u002Fstd-string-search","02.cpp\u002F43.std-string-search",{"title":1889,"path":1890,"stem":1891},"std::string_view: невласницький погляд на рядок без копіювання","\u002Fcpp\u002Fstd-string-view","02.cpp\u002F44.std-string-view",{"title":1893,"path":1894,"stem":1895},"Об'єднання (union): один блок пам'яті, кілька інтерпретацій","\u002Fcpp\u002Funion","02.cpp\u002F45.union",{"title":1897,"path":1898,"stem":1899},"Організація коду: файли, препроцесор, простори імен","\u002Fcpp\u002Fmultifile-programs","02.cpp\u002F46.multifile-programs",{"title":1901,"path":1902,"stem":1903},"Робота з файлами: C-стиль (stdio.h)","\u002Fcpp\u002Fc-style-files","02.cpp\u002F47.c-style-files",{"title":1905,"path":1906,"stem":1907},"Робота з файлами: C++-стиль (fstream)","\u002Fcpp\u002Fcpp-style-files","02.cpp\u002F48.cpp-style-files",{"title":1909,"path":1910,"stem":1911},"Введення в ООП: від процедур до об'єктів","\u002Fcpp\u002Foop-intro","02.cpp\u002F49.oop-intro",{"title":1913,"path":1914,"stem":1915},"Класи, об'єкти та методи","\u002Fcpp\u002Fclasses-objects-methods","02.cpp\u002F50.classes-objects-methods",{"title":1917,"path":1918,"stem":1919},"Специфікатори доступу та інкапсуляція","\u002Fcpp\u002Faccess-specifiers-encapsulation","02.cpp\u002F51.access-specifiers-encapsulation",{"title":1921,"path":1922,"stem":1923},"Конструктори: гарантована ініціалізація об'єктів","\u002Fcpp\u002Fconstructors","02.cpp\u002F52.constructors",{"title":1925,"path":1926,"stem":1927},"Списки ініціалізації та делегуючі конструктори","\u002Fcpp\u002Finit-lists-delegating","02.cpp\u002F53.init-lists-delegating",{"title":1929,"path":1930,"stem":1931},"Деструктори та ідіома RAII","\u002Fcpp\u002Fdestructors-raii","02.cpp\u002F54.destructors-raii",{"title":1933,"path":1934,"stem":1935},"Прихований покажчик this","\u002Fcpp\u002Fthis-pointer","02.cpp\u002F55.this-pointer",{"title":1937,"path":1938,"stem":1939},"Розподіл класу на заголовковий файл та файл реалізації","\u002Fcpp\u002Fclass-header-source","02.cpp\u002F56.class-header-source",{"title":1941,"path":1942,"stem":1943},"Класи та const","\u002Fcpp\u002Fconst-classes","02.cpp\u002F57.const-classes",{"title":1945,"path":1946,"stem":1947},"Статичні члени класу","\u002Fcpp\u002Fstatic-members","02.cpp\u002F58.static-members",{"title":1949,"path":1950,"stem":1951},"Конструктор копіювання та запобігання копіюванню","\u002Fcpp\u002Fcopy-constructor","02.cpp\u002F59.copy-constructor",{"title":1953,"path":1954,"stem":1955},"Дружні функції та класи","\u002Fcpp\u002Ffriend-functions-classes","02.cpp\u002F60.friend-functions-classes",{"title":1957,"path":1958,"stem":1959},"Анонімні об'єкти та вкладені типи","\u002Fcpp\u002Fanonymous-objects-nested-types","02.cpp\u002F61.anonymous-objects-nested-types",{"title":1961,"path":1962,"stem":1963},"Вимірювання часу виконання: RAII-таймер","\u002Fcpp\u002Ftiming-raii-timer","02.cpp\u002F62.timing-raii-timer",{"title":1965,"path":1966,"stem":1967},"Перевантаження операторів: основи та арифметика","\u002Fcpp\u002Foperator-overloading-basics","02.cpp\u002F63.operator-overloading-basics",{"title":1969,"path":1970,"stem":1971},"Перевантаження операторів: методи класу та спеціальні оператори","\u002Fcpp\u002Foperator-overloading-methods","02.cpp\u002F64.operator-overloading-methods",{"title":1973,"path":1974,"stem":1975},"Приведення типів, explicit та delete","\u002Fcpp\u002Ftypecast-explicit-delete","02.cpp\u002F65.typecast-explicit-delete",{"title":1977,"path":1978,"stem":1979},"Глибоке копіювання та оператор присвоювання","\u002Fcpp\u002Fdeep-copy-assignment","02.cpp\u002F66.deep-copy-assignment",{"title":1981,"path":1982,"stem":1983},"Зв'язки між об'єктами: композиція та агрегація","\u002Fcpp\u002Fcomposition-aggregation","02.cpp\u002F67.composition-aggregation",{"title":1985,"path":1986,"stem":1987},"Асоціація та залежність між об","\u002Fcpp\u002Fassociation-dependency","02.cpp\u002F68.association-dependency",{"title":1989,"path":1990,"stem":1991},"Контейнерні класи та `std::initializer_list`","\u002Fcpp\u002Fcontainer-classes-initializer-list","02.cpp\u002F69.container-classes-initializer-list",{"title":1993,"path":1994,"stem":1995},"Вступ до наслідування: базові поняття та відношення «is-a»","\u002Fcpp\u002Finheritance-intro","02.cpp\u002F70.inheritance-intro",{"title":1997,"path":1998,"stem":1999},"Конструктори, деструктори та порядок ініціалізації в ієрархіях","\u002Fcpp\u002Finheritance-constructors-destructors","02.cpp\u002F71.inheritance-constructors-destructors",{"title":2001,"path":2002,"stem":2003},"Специфікатор `protected` та види наслідування","\u002Fcpp\u002Finheritance-access-specifiers","02.cpp\u002F72.inheritance-access-specifiers",{"title":2005,"path":2006,"stem":2007},"Перевизначення методів та приховування імен","\u002Fcpp\u002Fmethod-overriding-hiding","02.cpp\u002F73.method-overriding-hiding",{"title":2009,"path":2010,"stem":2011},"Множинне наслідування та проблема ромба","\u002Fcpp\u002Fmultiple-inheritance-diamond","02.cpp\u002F74.multiple-inheritance-diamond",{"title":2013,"path":2014,"stem":2015},"Вказівники на базовий клас та віртуальні функції","\u002Fcpp\u002Fvirtual-functions-polymorphism","02.cpp\u002F75.virtual-functions-polymorphism",{"title":2017,"path":2018,"stem":2019},"Специфікатори override, final та віртуальні деструктори","\u002Fcpp\u002Foverride-final-virtual-destructors","02.cpp\u002F76.override-final-virtual-destructors",{"title":2021,"path":2022,"stem":2023},"Внутрішні механізми поліморфізму: vtable, vptr та зв'язування","\u002Fcpp\u002Fvirtual-table-binding","02.cpp\u002F77.virtual-table-binding",{"title":2025,"path":2026,"stem":2027},"Абстрактні класи та чисті віртуальні функції","\u002Fcpp\u002Fabstract-classes-interfaces","02.cpp\u002F78.abstract-classes-interfaces",{"title":2029,"path":2030,"stem":2031},"Зрізка об'єктів, віртуальне наслідування та поліморфний вивід","\u002Fcpp\u002Fslicing-virtual-base-io","02.cpp\u002F79.slicing-virtual-base-io",{"title":2033,"path":2034,"stem":2035},"Динамічне приведення типів (`dynamic_cast`) та RTTI","\u002Fcpp\u002Fdynamic-cast-rtti","02.cpp\u002F80.dynamic-cast-rtti",{"title":2037,"path":2038,"stem":2039},"Шаблони функцій: узагальнене програмування","\u002Fcpp\u002Ffunction-templates","02.cpp\u002F81.function-templates",{"title":2041,"path":2042,"stem":2043},"Шаблони класів та NTTP-параметри","\u002Fcpp\u002Fclass-templates-nttp","02.cpp\u002F82.class-templates-nttp",{"title":2045,"path":2046,"stem":2047},"Повна (явна) спеціалізація шаблонів","\u002Fcpp\u002Ffull-template-specialization","02.cpp\u002F83.full-template-specialization",{"title":2049,"path":2050,"stem":2051},"Часткова спеціалізація шаблонів класів","\u002Fcpp\u002Fpartial-template-specialization","02.cpp\u002F84.partial-template-specialization",{"title":2053,"path":2054,"stem":2055},"Механізм винятків: try, catch, throw та розкручування стеку","\u002Fcpp\u002Fexceptions-basics","02.cpp\u002F85.exceptions-basics",{"title":2057,"path":2058,"stem":2059},"Ієрархія std::exception та власні класи винятків","\u002Fcpp\u002Fexception-hierarchy-custom","02.cpp\u002F86.exception-hierarchy-custom",{"title":2061,"path":2062,"stem":2063},"Повторне генерування винятків та функціональний try-блок","\u002Fcpp\u002Frethrow-function-try","02.cpp\u002F87.rethrow-function-try",{"title":2065,"path":2066,"stem":2067},"Безпека винятків, noexcept та деструктори","\u002Fcpp\u002Fsafety-noexcept","02.cpp\u002F88.safety-noexcept",{"title":2069,"path":2070,"stem":2071},"Стаття 89 — Власні структури даних: стек та черга","\u002Fcpp\u002Fcustom-stack-queue","02.cpp\u002F89.custom-stack-queue",{"title":2073,"path":2074,"stem":2075},"Стаття 90 — Власні структури даних: однозв'язаний список","\u002Fcpp\u002Flinked-list","02.cpp\u002F90.linked-list",{"title":2077,"path":2078,"stem":2079},"Стаття 91 — Власні структури даних: бінарне дерево пошуку","\u002Fcpp\u002Fbinary-search-tree","02.cpp\u002F91.binary-search-tree",{"title":2081,"path":2082,"stem":2083},"Стаття 92 — Власні структури даних: хеш-таблиця","\u002Fcpp\u002Fhash-table","02.cpp\u002F92.hash-table",{"title":2085,"icon":2086,"path":2087,"stem":2088,"children":2089,"page":59},"JavaScript","i-devicon-javascript","\u002Fjavascript","03.javascript",[2090,2116,2170,2192,2496,2534,2552],{"title":2091,"icon":2092,"path":2093,"stem":2094,"children":2095,"page":59},"Events","i-lucide-mouse-pointer-click","\u002Fjavascript\u002Fevents","03.javascript\u002F01.events",[2096,2100,2104,2108,2112],{"title":2097,"path":2098,"stem":2099},"Вступ до подій браузера","\u002Fjavascript\u002Fevents\u002Fintro","03.javascript\u002F01.events\u002F01.intro",{"title":2101,"path":2102,"stem":2103},"Бульбашковий механізм (Bubbling) та занурення (Capturing)","\u002Fjavascript\u002Fevents\u002Fbubbling-capturing","03.javascript\u002F01.events\u002F02.bubbling-capturing",{"title":2105,"path":2106,"stem":2107},"Делегування подій (Event Delegation)","\u002Fjavascript\u002Fevents\u002Fdelegate-events","03.javascript\u002F01.events\u002F03.delegate-events",{"title":2109,"path":2110,"stem":2111},"Типові дії браузера та preventDefault()","\u002Fjavascript\u002Fevents\u002Fprevent-default","03.javascript\u002F01.events\u002F04.prevent-default",{"title":2113,"path":2114,"stem":2115},"Запуск користувацьких подій (Custom Events)","\u002Fjavascript\u002Fevents\u002Fcustom-events","03.javascript\u002F01.events\u002F05.custom-events",{"title":2117,"icon":2118,"path":2119,"stem":2120,"children":2121,"page":59},"Network","i-lucide-globe","\u002Fjavascript\u002Fnetwork","03.javascript\u002F02.network",[2122,2126,2130,2134,2138,2142,2146,2150,2154,2158,2162,2166],{"title":2123,"path":2124,"stem":2125},"Fetch API - Сучасний підхід до HTTP-запитів","\u002Fjavascript\u002Fnetwork\u002F01-fetch-api","03.javascript\u002F02.network\u002F01-fetch-api",{"title":2127,"path":2128,"stem":2129},"FormData - Робота з формами та файлами","\u002Fjavascript\u002Fnetwork\u002F02-formdata","03.javascript\u002F02.network\u002F02-formdata",{"title":2131,"path":2132,"stem":2133},"Відстеження прогресу завантаження","\u002Fjavascript\u002Fnetwork\u002F03-download-progress","03.javascript\u002F02.network\u002F03-download-progress",{"title":2135,"path":2136,"stem":2137},"Переривання fetch-запитів","\u002Fjavascript\u002Fnetwork\u002F04-abort-requests","03.javascript\u002F02.network\u002F04-abort-requests",{"title":2139,"path":2140,"stem":2141},"CORS - Запити між різними джерелами","\u002Fjavascript\u002Fnetwork\u002F05-cors","03.javascript\u002F02.network\u002F05-cors",{"title":2143,"path":2144,"stem":2145},"Fetch API - Повний довідник опцій","\u002Fjavascript\u002Fnetwork\u002F06-fetch-options","03.javascript\u002F02.network\u002F06-fetch-options",{"title":2147,"path":2148,"stem":2149},"URL Objects - Робота з посиланнями","\u002Fjavascript\u002Fnetwork\u002F07-url-objects","03.javascript\u002F02.network\u002F07-url-objects",{"title":2151,"path":2152,"stem":2153},"XMLHttpRequest - AJAX та низькорівневі запити","\u002Fjavascript\u002Fnetwork\u002F08-xmlhttprequest","03.javascript\u002F02.network\u002F08-xmlhttprequest",{"title":2155,"path":2156,"stem":2157},"Відновлюване завантаження файлів","\u002Fjavascript\u002Fnetwork\u002F09-resumable-upload","03.javascript\u002F02.network\u002F09-resumable-upload",{"title":2159,"path":2160,"stem":2161},"Cookies, document.cookie та світ після \"Cookiepocalypse\"","\u002Fjavascript\u002Fnetwork\u002F10-cookies","03.javascript\u002F02.network\u002F10-cookies",{"title":2163,"path":2164,"stem":2165},"js-cookie: Керування Cookies без Болю","\u002Fjavascript\u002Fnetwork\u002F11-js-cookie","03.javascript\u002F02.network\u002F11-js-cookie",{"title":2167,"path":2168,"stem":2169},"Axios: Потужний HTTP-клієнт для JavaScript","\u002Fjavascript\u002Fnetwork\u002F12-axios","03.javascript\u002F02.network\u002F12-axios",{"title":2171,"icon":2172,"path":2173,"stem":2174,"children":2175,"page":59},"Bom","i-lucide-monitor","\u002Fjavascript\u002Fbom","03.javascript\u002F03.bom",[2176,2180,2184,2188],{"title":2177,"path":2178,"stem":2179},"LocalStorage, SessionStorage та patterns збереження даних","\u002Fjavascript\u002Fbom\u002F01-localstorage","03.javascript\u002F03.bom\u002F01-localstorage",{"title":2181,"path":2182,"stem":2183},"Location Object - Керування адресою сторінки","\u002Fjavascript\u002Fbom\u002F02-location-object","03.javascript\u002F03.bom\u002F02-location-object",{"title":2185,"path":2186,"stem":2187},"History API - Керування історією браузера","\u002Fjavascript\u002Fbom\u002F03-history-api","03.javascript\u002F03.bom\u002F03-history-api",{"title":2189,"path":2190,"stem":2191},"Navigator Object - Ідентифікація та Можливості Пристрою","\u002Fjavascript\u002Fbom\u002F04-navigator-object","03.javascript\u002F03.bom\u002F04-navigator-object",{"title":2193,"icon":2194,"path":2195,"stem":2196,"children":2197},"React","i-devicon-react","\u002Fjavascript\u002Freact","03.javascript\u002F04.react\u002Findex",[2198,2199,2203,2207,2211,2215,2278,2313,2465],{"title":2193,"path":2195,"stem":2196},{"title":2200,"path":2201,"stem":2202},"Робота з Формами в React","\u002Fjavascript\u002Freact\u002Freact-forms","03.javascript\u002F04.react\u002F01.react-forms",{"title":2204,"path":2205,"stem":2206},"React Hook Form: Професійна Робота з Формами","\u002Fjavascript\u002Freact\u002Freact-hook-form","03.javascript\u002F04.react\u002F02.react-hook-form",{"title":2208,"path":2209,"stem":2210},"React Hook Form: Глибоке Розуміння Архітектури та Оптимізації","\u002Fjavascript\u002Freact\u002Freact-hook-form-new","03.javascript\u002F04.react\u002F02.react-hook-form-new",{"title":2212,"path":2213,"stem":2214},"Axios та React: Професійна Архітектура Запитів","\u002Fjavascript\u002Freact\u002Fdata-fetching-axios","03.javascript\u002F04.react\u002F03.data-fetching-axios",{"title":2216,"icon":132,"path":2217,"stem":2218,"children":2219},"Tanstack Query","\u002Fjavascript\u002Freact\u002Ftanstack-query","03.javascript\u002F04.react\u002F04.tanstack-query\u002Findex",[2220,2222,2226,2230,2234,2238,2242,2246,2250,2254,2258,2262,2266,2270,2274],{"title":2221,"path":2217,"stem":2218},"TanStack Query: Майстерність Керування Станом Сервера",{"title":2223,"path":2224,"stem":2225},"Парадигма Server State: Чому useEffect недостатньо","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fserver-state-paradigm","03.javascript\u002F04.react\u002F04.tanstack-query\u002F01.server-state-paradigm",{"title":2227,"path":2228,"stem":2229},"Встановлення та Налаштування: Фундамент","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Finstallation-and-devtools","03.javascript\u002F04.react\u002F04.tanstack-query\u002F02.installation-and-devtools",{"title":2231,"path":2232,"stem":2233},"Основи Запитів та Магія Ключів","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fquery-basics-and-keys","03.javascript\u002F04.react\u002F04.tanstack-query\u002F03.query-basics-and-keys",{"title":2235,"path":2236,"stem":2237},"Синхронізація Даних: Життєвий Цикл Запиту","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fdata-synchronization","03.javascript\u002F04.react\u002F04.tanstack-query\u002F04.data-synchronization",{"title":2239,"path":2240,"stem":2241},"Мутації та Інвалідація: Зміна Даних","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fmutations-and-invalidation","03.javascript\u002F04.react\u002F04.tanstack-query\u002F05.mutations-and-invalidation",{"title":2243,"path":2244,"stem":2245},"Оптимістичні Оновлення: Швидше за Світло","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Foptimistic-updates","03.javascript\u002F04.react\u002F04.tanstack-query\u002F06.optimistic-updates",{"title":2247,"path":2248,"stem":2249},"Пагінація та Infinite Scroll","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fpagination-and-load-more","03.javascript\u002F04.react\u002F04.tanstack-query\u002F07.pagination-and-load-more",{"title":2251,"path":2252,"stem":2253},"Просунуті Патерни та Оптимізація","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fadvanced-patterns","03.javascript\u002F04.react\u002F04.tanstack-query\u002F08.advanced-patterns",{"title":2255,"path":2256,"stem":2257},"Архітектура та Best Practices","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Farchitecture-and-best-practices","03.javascript\u002F04.react\u002F04.tanstack-query\u002F09.architecture-and-best-practices",{"title":2259,"path":2260,"stem":2261},"Server-Side Rendering (SSR) та Гідратація","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fserver-side-rendering","03.javascript\u002F04.react\u002F04.tanstack-query\u002F10.server-side-rendering",{"title":2263,"path":2264,"stem":2265},"Стратегії Тестування","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Ftesting-strategies","03.javascript\u002F04.react\u002F04.tanstack-query\u002F11.testing-strategies",{"title":2267,"path":2268,"stem":2269},"Аутентифікація та Обробка Помилок","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fauthentication-and-errors","03.javascript\u002F04.react\u002F04.tanstack-query\u002F12.authentication-and-errors",{"title":2271,"path":2272,"stem":2273},"React Suspense та Майбутнє","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Freact-suspense","03.javascript\u002F04.react\u002F04.tanstack-query\u002F13.react-suspense",{"title":2275,"path":2276,"stem":2277},"Глибоке Занурення в Продуктивність","\u002Fjavascript\u002Freact\u002Ftanstack-query\u002Fperformance-deep-dive","03.javascript\u002F04.react\u002F04.tanstack-query\u002F14.performance-deep-dive",{"title":2279,"icon":2194,"path":2280,"stem":2281,"children":2282},"React Router","\u002Fjavascript\u002Freact\u002Freact-router","03.javascript\u002F04.react\u002F05.react-router\u002Findex",[2283,2285,2289,2293,2297,2301,2305,2309],{"title":2284,"path":2280,"stem":2281},"React Router: Навігаційна система сучасного вебу",{"title":2286,"path":2287,"stem":2288},"Налаштування та Базовий Роутинг","\u002Fjavascript\u002Freact\u002Freact-router\u002Fsetup-and-basic-routing","03.javascript\u002F04.react\u002F05.react-router\u002F01.setup-and-basic-routing",{"title":2290,"path":2291,"stem":2292},"Динамічна Навігація","\u002Fjavascript\u002Freact\u002Freact-router\u002Fnavigation-and-links","03.javascript\u002F04.react\u002F05.react-router\u002F02.navigation-and-links",{"title":2294,"path":2295,"stem":2296},"Вкладені Маршрути та Макети","\u002Fjavascript\u002Freact\u002Freact-router\u002Fnested-routes-and-layouts","03.javascript\u002F04.react\u002F05.react-router\u002F03.nested-routes-and-layouts",{"title":2298,"path":2299,"stem":2300},"Динамічні Маршрути та Параметри","\u002Fjavascript\u002Freact\u002Freact-router\u002Fdynamic-routing","03.javascript\u002F04.react\u002F05.react-router\u002F04.dynamic-routing",{"title":2302,"path":2303,"stem":2304},"Data APIs: Loaders та Actions","\u002Fjavascript\u002Freact\u002Freact-router\u002Fdata-loading","03.javascript\u002F04.react\u002F05.react-router\u002F05.data-loading",{"title":2306,"path":2307,"stem":2308},"Просунуті Патерни","\u002Fjavascript\u002Freact\u002Freact-router\u002Fadvanced-patterns","03.javascript\u002F04.react\u002F05.react-router\u002F06.advanced-patterns",{"title":2310,"path":2311,"stem":2312},"Legacy Routing: Компонентний підхід","\u002Fjavascript\u002Freact\u002Freact-router\u002Flegacy-routing","03.javascript\u002F04.react\u002F05.react-router\u002F07.legacy-routing",{"title":2314,"icon":132,"path":2315,"stem":2316,"children":2317},"Redux","\u002Fjavascript\u002Freact\u002Fredux","03.javascript\u002F04.react\u002F06.redux\u002Findex",[2318,2320,2336,2365,2374,2395,2411,2440],{"title":2319,"path":2315,"stem":2316},"Redux: Еволюція управління станом",{"title":14,"icon":15,"path":2321,"stem":2322,"children":2323,"page":59},"\u002Fjavascript\u002Freact\u002Fredux\u002Ffundamentals","03.javascript\u002F04.react\u002F06.redux\u002F01.fundamentals",[2324,2328,2332],{"title":2325,"path":2326,"stem":2327},"Вступ до State Management","\u002Fjavascript\u002Freact\u002Fredux\u002Ffundamentals\u002Fintro-state-management","03.javascript\u002F04.react\u002F06.redux\u002F01.fundamentals\u002F01.intro-state-management",{"title":2329,"path":2330,"stem":2331},"Філософія Redux та Три Принципи","\u002Fjavascript\u002Freact\u002Fredux\u002Ffundamentals\u002Fredux-philosophy","03.javascript\u002F04.react\u002F06.redux\u002F01.fundamentals\u002F02.redux-philosophy",{"title":2333,"path":2334,"stem":2335},"Чисті функції та Іммутабельність","\u002Fjavascript\u002Freact\u002Fredux\u002Ffundamentals\u002Fpure-functions-immutability","03.javascript\u002F04.react\u002F06.redux\u002F01.fundamentals\u002F03.pure-functions-immutability",{"title":2337,"icon":132,"path":2338,"stem":2339,"children":2340,"page":59},"Classic Redux","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux",[2341,2345,2349,2353,2357,2361],{"title":2342,"path":2343,"stem":2344},"Створення Store (Classic Redux)","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Fstore-setup","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F01.store-setup",{"title":2346,"path":2347,"stem":2348},"Actions, Constants та Action Creators","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Factions-constants","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F02.actions-constants",{"title":2350,"path":2351,"stem":2352},"Логіка Reducers","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Freducers","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F03.reducers",{"title":2354,"path":2355,"stem":2356},"Комбінування Reducers (Root Reducer)","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Fdata-flow","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F04.data-flow",{"title":2358,"path":2359,"stem":2360},"Підключення до React (React-Redux)","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Freact-redux-connection","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F05.react-redux-connection",{"title":2362,"path":2363,"stem":2364},"Middleware та Асинхронність (Redux Thunk)","\u002Fjavascript\u002Freact\u002Fredux\u002Fclassic-redux\u002Fmiddleware-thunk","03.javascript\u002F04.react\u002F06.redux\u002F02.classic-redux\u002F06.middleware-thunk",{"title":2366,"icon":132,"path":2367,"stem":2368,"children":2369,"page":59},"Transition To Rtk","\u002Fjavascript\u002Freact\u002Fredux\u002Ftransition-to-rtk","03.javascript\u002F04.react\u002F06.redux\u002F03.transition-to-rtk",[2370],{"title":2371,"path":2372,"stem":2373},"Проблеми класичного Redux","\u002Fjavascript\u002Freact\u002Fredux\u002Ftransition-to-rtk\u002Fproblems-with-classic","03.javascript\u002F04.react\u002F06.redux\u002F03.transition-to-rtk\u002F01.problems-with-classic",{"title":2375,"icon":132,"path":2376,"stem":2377,"children":2378,"page":59},"Redux Toolkit","\u002Fjavascript\u002Freact\u002Fredux\u002Fredux-toolkit","03.javascript\u002F04.react\u002F06.redux\u002F04.redux-toolkit",[2379,2383,2387,2391],{"title":2380,"path":2381,"stem":2382},"Налаштування Store з configureStore","\u002Fjavascript\u002Freact\u002Fredux\u002Fredux-toolkit\u002Fconfigure-store","03.javascript\u002F04.react\u002F06.redux\u002F04.redux-toolkit\u002F01.configure-store",{"title":2384,"path":2385,"stem":2386},"createSlice: Революція в Redux","\u002Fjavascript\u002Freact\u002Fredux\u002Fredux-toolkit\u002Fcreate-slice","03.javascript\u002F04.react\u002F06.redux\u002F04.redux-toolkit\u002F02.create-slice",{"title":2388,"path":2389,"stem":2390},"Асинхронність з createAsyncThunk","\u002Fjavascript\u002Freact\u002Fredux\u002Fredux-toolkit\u002Fasync-thunks","03.javascript\u002F04.react\u002F06.redux\u002F04.redux-toolkit\u002F03.async-thunks",{"title":2392,"path":2393,"stem":2394},"04. Entity Adapter: Керування нормалізованим станом","\u002Fjavascript\u002Freact\u002Fredux\u002Fredux-toolkit\u002Fentity-adapter","03.javascript\u002F04.react\u002F06.redux\u002F04.redux-toolkit\u002F04.entity-adapter",{"title":2396,"icon":92,"path":2397,"stem":2398,"children":2399,"page":59},"Advanced","\u002Fjavascript\u002Freact\u002Fredux\u002Fadvanced","03.javascript\u002F04.react\u002F06.redux\u002F05.advanced",[2400,2404,2408],{"title":2401,"path":2402,"stem":2403},"Мемоізація та Селектори: Повний Гайд по Reselect","\u002Fjavascript\u002Freact\u002Fredux\u002Fadvanced\u002Fselectors-reselect","03.javascript\u002F04.react\u002F06.redux\u002F05.advanced\u002F01.selectors-reselect",{"title":2405,"path":2406,"stem":2407},"RTK Query: Архітектура Серверного Кешу","\u002Fjavascript\u002Freact\u002Fredux\u002Fadvanced\u002Frtk-query-intro","03.javascript\u002F04.react\u002F06.redux\u002F05.advanced\u002F02.rtk-query-intro",{"title":2255,"path":2409,"stem":2410},"\u002Fjavascript\u002Freact\u002Fredux\u002Fadvanced\u002Farchitecture-best-practices","03.javascript\u002F04.react\u002F06.redux\u002F05.advanced\u002F03.architecture-best-practices",{"title":2412,"icon":132,"path":2413,"stem":2414,"children":2415,"page":59},"Project Kanban","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban",[2416,2420,2424,2428,2432,2436],{"title":2417,"path":2418,"stem":2419},"Проєкт: Kanban Board (Trello Clone)","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Fproject-overview","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F01.project-overview",{"title":2421,"path":2422,"stem":2423},"Налаштування та Типізація","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Fsetup-and-types","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F02.setup-and-types",{"title":2425,"path":2426,"stem":2427},"Board Slice: Серце Дошки","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Fboard-slice","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F03.board-slice",{"title":2429,"path":2430,"stem":2431},"Логіка Drag & Drop","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Fdrag-and-drop-logic","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F04.drag-and-drop-logic",{"title":2433,"path":2434,"stem":2435},"Інтеграція з RTK Query","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Frtk-query-integration","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F05.rtk-query-integration",{"title":2437,"path":2438,"stem":2439},"Optimistic Updates","\u002Fjavascript\u002Freact\u002Fredux\u002Fproject-kanban\u002Foptimistic-updates","03.javascript\u002F04.react\u002F06.redux\u002F06.project-kanban\u002F06.optimistic-updates",{"title":2441,"icon":132,"path":2442,"stem":2443,"children":2444,"page":59},"Testing","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting","03.javascript\u002F04.react\u002F06.redux\u002F07.testing",[2445,2449,2453,2457,2461],{"title":2446,"path":2447,"stem":2448},"Тестування Redux","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting\u002Fintro-testing","03.javascript\u002F04.react\u002F06.redux\u002F07.testing\u002F01.intro-testing",{"title":2450,"path":2451,"stem":2452},"Тестування Reducers","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting\u002Ftesting-reducers","03.javascript\u002F04.react\u002F06.redux\u002F07.testing\u002F02.testing-reducers",{"title":2454,"path":2455,"stem":2456},"Тестування Селекторів","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting\u002Ftesting-selectors","03.javascript\u002F04.react\u002F06.redux\u002F07.testing\u002F03.testing-selectors",{"title":2458,"path":2459,"stem":2460},"Тестування Компонентів (Integration)","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting\u002Ftesting-components","03.javascript\u002F04.react\u002F06.redux\u002F07.testing\u002F04.testing-components",{"title":2462,"path":2463,"stem":2464},"Тестування Async Thunks","\u002Fjavascript\u002Freact\u002Fredux\u002Ftesting\u002Ftesting-thunks","03.javascript\u002F04.react\u002F06.redux\u002F07.testing\u002F05.testing-thunks",{"title":2466,"icon":132,"path":2467,"stem":2468,"children":2469},"Ui Libraries","\u002Fjavascript\u002Freact\u002Fui-libraries","03.javascript\u002F04.react\u002F07.ui-libraries\u002Findex",[2470,2472,2476,2480,2484,2488,2492],{"title":2471,"path":2467,"stem":2468},"UI Бібліотеки в React",{"title":2473,"path":2474,"stem":2475},"Вступ до UI Бібліотек: Навіщо Винаходити Велосипед Двічі?","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fintroduction-to-ui-libraries","03.javascript\u002F04.react\u002F07.ui-libraries\u002F01.introduction-to-ui-libraries",{"title":2477,"path":2478,"stem":2479},"Філософія shadcn\u002Fui: \"Not a Component Library\"","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fshadcn-philosophy","03.javascript\u002F04.react\u002F07.ui-libraries\u002F02.shadcn-philosophy",{"title":2481,"path":2482,"stem":2483},"Установка та Налаштування shadcn\u002Fui","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fshadcn-installation","03.javascript\u002F04.react\u002F07.ui-libraries\u002F03.shadcn-installation",{"title":2485,"path":2486,"stem":2487},"Базові Компоненти shadcn\u002Fui: Фундамент Інтерфейсу","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fshadcn-components-basics","03.javascript\u002F04.react\u002F07.ui-libraries\u002F04.shadcn-components-basics",{"title":2489,"path":2490,"stem":2491},"Компоненти Форм: Побудова Інтерактивних Form","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fshadcn-components-forms","03.javascript\u002F04.react\u002F07.ui-libraries\u002F05.shadcn-components-forms",{"title":2493,"path":2494,"stem":2495},"Складні Компоненти: Dialog, Dropdown, Table та Command","\u002Fjavascript\u002Freact\u002Fui-libraries\u002Fshadcn-components-advanced","03.javascript\u002F04.react\u002F07.ui-libraries\u002F06.shadcn-components-advanced",{"title":2497,"icon":2498,"path":2499,"stem":2500,"children":2501,"page":59},"TypeScript","i-devicon-typescript","\u002Fjavascript\u002Ftypescript","03.javascript\u002F05.typescript",[2502,2506,2510,2514,2518,2522,2526,2530],{"title":2503,"path":2504,"stem":2505},"TypeScript: Броня для вашого коду","\u002Fjavascript\u002Ftypescript\u002Fintro-and-basic-types","03.javascript\u002F05.typescript\u002F01.intro-and-basic-types",{"title":2507,"path":2508,"stem":2509},"Майстерність Моделювання Даних: Інтерфейси та Просунуті Типи","\u002Fjavascript\u002Ftypescript\u002Finterfaces-and-advanced-types","03.javascript\u002F05.typescript\u002F02.interfaces-and-advanced-types",{"title":2511,"path":2512,"stem":2513},"Алхімія Типів: Generics та Utility Types","\u002Fjavascript\u002Ftypescript\u002Fgenerics-and-utilities","03.javascript\u002F05.typescript\u002F03.generics-and-utilities",{"title":2515,"path":2516,"stem":2517},"Архітектура та Шаблони: Класи в TypeScript","\u002Fjavascript\u002Ftypescript\u002Fclasses-and-oop","03.javascript\u002F05.typescript\u002F04.classes-and-oop",{"title":2519,"path":2520,"stem":2521},"Продакшн та Екосистема: Advanced Config & Workflow","\u002Fjavascript\u002Ftypescript\u002Fadvanced-patterns-and-config","03.javascript\u002F05.typescript\u002F05.advanced-patterns-and-config",{"title":2523,"path":2524,"stem":2525},"TypeScript у світі React","\u002Fjavascript\u002Ftypescript\u002Freact-basics","03.javascript\u002F05.typescript\u002F06.react-basics",{"title":2527,"path":2528,"stem":2529},"React + TypeScript: Продвинуті патерни","\u002Fjavascript\u002Ftypescript\u002Freact-advanced","03.javascript\u002F05.typescript\u002F07.react-advanced",{"title":2531,"path":2532,"stem":2533},"React + TypeScript: Екосистема та бібліотеки","\u002Fjavascript\u002Ftypescript\u002Freact-ecosystem","03.javascript\u002F05.typescript\u002F08.react-ecosystem",{"title":2535,"icon":2536,"path":2537,"stem":2538,"children":2539,"page":59},"Асинхронність","i-lucide-refresh-cw","\u002Fjavascript\u002Fasync-programming","03.javascript\u002F06.async-programming",[2540,2544,2548],{"title":2541,"path":2542,"stem":2543},"Event Loop: серце асинхронності в JavaScript","\u002Fjavascript\u002Fasync-programming\u002Fevent-loop-fundamentals","03.javascript\u002F06.async-programming\u002F01.event-loop-fundamentals",{"title":2545,"path":2546,"stem":2547},"Task Queue та Microtask Queue: пріоритети виконання","\u002Fjavascript\u002Fasync-programming\u002Ftask-queues","03.javascript\u002F06.async-programming\u002F02.task-queues",{"title":2549,"path":2550,"stem":2551},"Асинхронні патерни: від Callbacks до Async\u002FAwait","\u002Fjavascript\u002Fasync-programming\u002Fasync-patterns","03.javascript\u002F06.async-programming\u002F03.async-patterns",{"title":2553,"path":2554,"stem":2555},"Atomic Design","\u002Fjavascript\u002Fatomic-design","03.javascript\u002F2.atomic-design",{"title":2557,"icon":2558,"path":2559,"stem":2560,"children":2561,"page":59},"Java","i-devicon-java","\u002Fjava","04.java",[2562,2565,2568,2572,2576,2580,2584],{"title":162,"path":2563,"stem":2564},"\u002Fjava\u002Fdata-mapper-part1","04.java\u002F01.data-mapper-part1",{"title":166,"path":2566,"stem":2567},"\u002Fjava\u002Fdata-mapper-part2","04.java\u002F02.data-mapper-part2",{"title":2569,"path":2570,"stem":2571},"Service Layer: Організація бізнес-логіки","\u002Fjava\u002Fservice-layer","04.java\u002F03.service-layer",{"title":2573,"path":2574,"stem":2575},"Rich Domain Model та State Pattern","\u002Fjava\u002Frich-domain-model","04.java\u002F04.rich-domain-model",{"title":2577,"path":2578,"stem":2579},"Патерни для складної бізнес-логіки","\u002Fjava\u002Fbusiness-logic-patterns","04.java\u002F05.business-logic-patterns",{"title":2581,"path":2582,"stem":2583},"Обробка помилок та валідація","\u002Fjava\u002Ferror-handling-validation","04.java\u002F06.error-handling-validation",{"title":2585,"path":2586,"stem":2587,"children":2588,"page":59},"Проектування баз даних","\u002Fjava\u002Fpr2","04.java\u002Fpr2",[2589,2593,2597,2601,2605,2609,2613,2617,2621,2625,2629,2633,2637,2641,2645,2649,2653,2657,2661,2665,2669,2673,2677,2681,2685,2689,2693,2697,2701,2705,2709,2713,2717,2721,2725,2729,2733],{"title":2590,"path":2591,"stem":2592},"Концептуальне моделювання: Мистецтво розуміння предметної області","\u002Fjava\u002Fpr2\u002Fconceptual-modeling","04.java\u002Fpr2\u002F01.conceptual-modeling",{"title":2594,"path":2595,"stem":2596},"Логічне моделювання: Від бізнес-ідей до структур даних","\u002Fjava\u002Fpr2\u002Flogical-modeling","04.java\u002Fpr2\u002F02.logical-modeling",{"title":2598,"path":2599,"stem":2600},"Нормалізація: Гігієна даних та боротьба з аномаліями","\u002Fjava\u002Fpr2\u002Fnormalization","04.java\u002Fpr2\u002F03.normalization",{"title":2602,"path":2603,"stem":2604},"Фізична схема: Від абстракції до DDL","\u002Fjava\u002Fpr2\u002Fphysical-schema","04.java\u002Fpr2\u002F04.physical-schema",{"title":2606,"path":2607,"stem":2608},"Архітектурна класифікація таблиць","\u002Fjava\u002Fpr2\u002Ftable-classification","04.java\u002Fpr2\u002F05.table-classification",{"title":2610,"path":2611,"stem":2612},"Database Migrations: Версіонування схеми з Flyway","\u002Fjava\u002Fpr2\u002Fdatabase-migrations","04.java\u002Fpr2\u002F06.database-migrations",{"title":2614,"path":2615,"stem":2616},"А що, якби це була не реляційна БД?","\u002Fjava\u002Fpr2\u002Fbeyond-relational","04.java\u002Fpr2\u002F07.beyond-relational",{"title":2618,"path":2619,"stem":2620},"Object-Relational Impedance Mismatch: Два світи, що не хочуть дружити","\u002Fjava\u002Fpr2\u002Fimpedance-mismatch","04.java\u002Fpr2\u002F09.impedance-mismatch",{"title":2622,"path":2623,"stem":2624},"JDBC: Перший контакт із базою даних","\u002Fjava\u002Fpr2\u002Fjdbc-fundamentals","04.java\u002Fpr2\u002F10.jdbc-fundamentals",{"title":2626,"path":2627,"stem":2628},"Якість коду: Spotless, SpotBugs та SonarQube","\u002Fjava\u002Fpr2\u002F10a.code-quality","04.java\u002Fpr2\u002F10a.code-quality",{"title":2630,"path":2631,"stem":2632},"Connection Pool: Патерн Object Pool для JDBC-з'єднань","\u002Fjava\u002Fpr2\u002Fconnection-pool","04.java\u002Fpr2\u002F11.connection-pool",{"title":2634,"path":2635,"stem":2636},"Row Data Gateway: Об'єкт як обгортка рядка таблиці","\u002Fjava\u002Fpr2\u002Frow-data-gateway","04.java\u002Fpr2\u002F12.row-data-gateway",{"title":2638,"path":2639,"stem":2640},"Table Data Gateway: Фасад таблиці як архітектурний відступ","\u002Fjava\u002Fpr2\u002Ftable-data-gateway","04.java\u002Fpr2\u002F13.table-data-gateway",{"title":2642,"path":2643,"stem":2644},"Repository + Data Mapper: Правильна шарова архітектура з JDBC","\u002Fjava\u002Fpr2\u002Frepository-data-mapper","04.java\u002Fpr2\u002F14.repository-data-mapper",{"title":2646,"path":2647,"stem":2648},"Identity Map: Кешування сутностей у рамках сесії","\u002Fjava\u002Fpr2\u002Fidentity-map","04.java\u002Fpr2\u002F15.identity-map",{"title":2650,"path":2651,"stem":2652},"Unit of Work: Відстеження змін і координація JDBC-транзакцій","\u002Fjava\u002Fpr2\u002Funit-of-work","04.java\u002Fpr2\u002F16.unit-of-work",{"title":2654,"path":2655,"stem":2656},"Strategy: Замінювані SQL-стратегії для підтримки різних СУБД","\u002Fjava\u002Fpr2\u002Fstrategy-sql","04.java\u002Fpr2\u002F17.strategy-sql",{"title":2658,"path":2659,"stem":2660},"Proxy: Lazy Loading для One-To-Many колекцій","\u002Fjava\u002Fpr2\u002Fproxy-lazy-loading","04.java\u002Fpr2\u002F18.proxy-lazy-loading",{"title":2662,"path":2663,"stem":2664},"Generic Repository через Java Reflection: анотації та динамічний SQL","\u002Fjava\u002Fpr2\u002Fgeneric-repository-reflection","04.java\u002Fpr2\u002F19.generic-repository-reflection",{"title":2666,"path":2667,"stem":2668},"Specification Pattern: Композиція бізнес-правил для складних запитів","\u002Fjava\u002Fpr2\u002Fspecification-pattern","04.java\u002Fpr2\u002F20.specification-pattern",{"title":2670,"path":2671,"stem":2672},"Розширені можливості Specification Pattern: підзапити, агрегації та гібридний підхід","\u002Fjava\u002Fpr2\u002F20a.advanced-specifications","04.java\u002Fpr2\u002F20a.advanced-specifications",{"title":2674,"path":2675,"stem":2676},"Асинхронність у JDBC: Від блокуючих викликів до CompletableFuture","\u002Fjava\u002Fpr2\u002Fasynchronous-jdbc","04.java\u002Fpr2\u002F21.asynchronous-jdbc",{"title":2678,"path":2679,"stem":2680},"Інтеграційне тестування JDBC-репозиторіїв: Embedded H2 та патерн AAA","\u002Fjava\u002Fpr2\u002Fintegration-testing-h2","04.java\u002Fpr2\u002F22.integration-testing-h2",{"title":2682,"path":2683,"stem":2684},"Testcontainers: Тестування з реальною PostgreSQL у Docker-контейнерах","\u002Fjava\u002Fpr2\u002Fintegration-testing-testcontainers","04.java\u002Fpr2\u002F23.integration-testing-testcontainers",{"title":2686,"path":2687,"stem":2688},"Google Guice: Впровадження залежностей у JavaFX-проєкті","\u002Fjava\u002Fpr2\u002Fdependency-injection-guice","04.java\u002Fpr2\u002F24.dependency-injection-guice",{"title":2690,"path":2691,"stem":2692},"JavaFX: Основи побудови графічних інтерфейсів","\u002Fjava\u002Fpr2\u002Fjavafx-fundamentals","04.java\u002Fpr2\u002F25.javafx-fundamentals",{"title":2694,"path":2695,"stem":2696},"Properties та Bindings: Реактивність у JavaFX","\u002Fjava\u002Fpr2\u002Fjavafx-properties-bindings","04.java\u002Fpr2\u002F26.javafx-properties-bindings",{"title":2698,"path":2699,"stem":2700},"MVC vs MVP vs MVVM: Еволюція архітектурних патернів UI","\u002Fjava\u002Fpr2\u002Fui-architecture-patterns","04.java\u002Fpr2\u002F27.ui-architecture-patterns",{"title":2702,"path":2703,"stem":2704},"MVVM на практиці: Побудова ViewModel","\u002Fjava\u002Fpr2\u002Fmvvm-viewmodel-implementation","04.java\u002Fpr2\u002F28.mvvm-viewmodel-implementation",{"title":2706,"path":2707,"stem":2708},"View та Controller: Зв'язування з ViewModel через FXML","\u002Fjava\u002Fpr2\u002Fmvvm-view-controller","04.java\u002Fpr2\u002F29.mvvm-view-controller",{"title":2710,"path":2711,"stem":2712},"Інтеграція MVVM з Guice: Автоматична ін'єкція залежностей","\u002Fjava\u002Fpr2\u002Fmvvm-guice-integration","04.java\u002Fpr2\u002F30.mvvm-guice-integration",{"title":2714,"path":2715,"stem":2716},"Валідація та обробка помилок у MVVM","\u002Fjava\u002Fpr2\u002Fmvvm-validation-error-handling","04.java\u002Fpr2\u002F31.mvvm-validation-error-handling",{"title":2718,"path":2719,"stem":2720},"Навігація та управління екранами у JavaFX MVVM","\u002Fjava\u002Fpr2\u002Fmvvm-navigation-screen-management","04.java\u002Fpr2\u002F32.mvvm-navigation-screen-management",{"title":2722,"path":2723,"stem":2724},"Тестування JavaFX MVVM-додатків","\u002Fjava\u002Fpr2\u002Fmvvm-testing","04.java\u002Fpr2\u002F33.mvvm-testing",{"title":2726,"path":2727,"stem":2728},"Стилізація та теми у JavaFX: CSS та User Experience","\u002Fjava\u002Fpr2\u002Fjavafx-styling-themes","04.java\u002Fpr2\u002F34.javafx-styling-themes",{"title":2730,"path":2731,"stem":2732},"AtlantaFX: Сучасні теми для JavaFX додатків","\u002Fjava\u002Fpr2\u002Fatlantafx-modern-themes","04.java\u002Fpr2\u002F35.atlantafx-modern-themes",{"title":2734,"path":2735,"stem":2736},"Пакування та розповсюдження JavaFX-додатків","\u002Fjava\u002Fpr2\u002Fjar-packaging-distribution","04.java\u002Fpr2\u002F36.jar-packaging-distribution",{"title":2738,"icon":2739,"path":2740,"stem":2741,"children":2742,"page":59},"Python","i-devicon-python","\u002Fpython","05.python",[2743,2747,2750,2754,2758,2762,2766,2770,2774,2778,2782,2786,2790,2794,2798,2859],{"title":2744,"path":2745,"stem":2746},"Модулі, Пакети та Віртуальні Середовища","\u002Fpython\u002Fmodules-packages-venv","05.python\u002F00.modules-packages-venv",{"title":71,"path":2748,"stem":2749},"\u002Fpython\u002Fclasses-objects","05.python\u002F01.classes-objects",{"title":2751,"path":2752,"stem":2753},"Інкапсуляція, Керування Доступом та Властивості","\u002Fpython\u002Fencapsulation","05.python\u002F02.encapsulation",{"title":2755,"path":2756,"stem":2757},"Наслідування, MRO та суперсила super()","\u002Fpython\u002Finheritance-mro","05.python\u002F03.inheritance-mro",{"title":2759,"path":2760,"stem":2761},"Абстракція — ABC проти Статичних Протоколів (PEP 544)","\u002Fpython\u002Fabstraction-protocols","05.python\u002F04.abstraction-protocols",{"title":2763,"path":2764,"stem":2765},"Магічні методи (Dunder) та Емуляція протоколів","\u002Fpython\u002Fdunder-methods","05.python\u002F05.dunder-methods",{"title":2767,"path":2768,"stem":2769},"Декоратори та Керування життєвим циклом методів","\u002Fpython\u002Fdecorators-static-class","05.python\u002F06.decorators-static-class",{"title":2771,"path":2772,"stem":2773},"Дескриптори — Магія доступу до атрибутів","\u002Fpython\u002Fdescriptors","05.python\u002F07.descriptors",{"title":2775,"path":2776,"stem":2777},"Метакласи — Динамічне створення класів під капотом CPython","\u002Fpython\u002Fmetaclasses","05.python\u002F08.metaclasses",{"title":2779,"path":2780,"stem":2781},"Dataclasses, NamedTuple та сучасні контейнери Python","\u002Fpython\u002Fmodern-containers","05.python\u002F09.modern-containers",{"title":2783,"path":2784,"stem":2785},"GIL та модель конкурентності CPython — фундамент перед потоками і процесами","\u002Fpython\u002Fgil-concurrency-intro","05.python\u002F11.gil-concurrency-intro",{"title":2787,"path":2788,"stem":2789},"Threading — конкурентність для I\u002FO-bound задач","\u002Fpython\u002Fthreading","05.python\u002F12.threading",{"title":2791,"path":2792,"stem":2793},"Multiprocessing — справжній паралелізм для CPU-bound задач","\u002Fpython\u002Fmultiprocessing","05.python\u002F13.multiprocessing",{"title":2795,"path":2796,"stem":2797},"asyncio — кооперативна конкурентність та event loop","\u002Fpython\u002Fasyncio","05.python\u002F14.asyncio",{"title":2799,"icon":92,"path":2800,"stem":2801,"children":2802,"page":59},"FastAPI","\u002Fpython\u002Ffastapi","05.python\u002Ffastapi",[2803,2807,2811,2815,2819,2823,2827,2831,2835,2839,2843,2847,2851,2855],{"title":2804,"path":2805,"stem":2806},"Глибокий Typing та Pydantic v2 — від анотацій до валідації","\u002Fpython\u002Ffastapi\u002Ftyping-pydantic","05.python\u002Ffastapi\u002F15.typing-pydantic",{"title":2808,"path":2809,"stem":2810},"WSGI, ASGI та Python Web-екосистема","\u002Fpython\u002Ffastapi\u002Fwsgi-asgi-ecosystem","05.python\u002Ffastapi\u002F16.wsgi-asgi-ecosystem",{"title":2812,"path":2813,"stem":2814},"FastAPI: Перший додаток, Uvicorn та OpenAPI","\u002Fpython\u002Ffastapi\u002Ffastapi-intro","05.python\u002Ffastapi\u002F17.fastapi-intro",{"title":2816,"path":2817,"stem":2818},"Маршрутизація, параметри запитів та APIRouter","\u002Fpython\u002Ffastapi\u002Ffastapi-routing-params","05.python\u002Ffastapi\u002F18.fastapi-routing-params",{"title":2820,"path":2821,"stem":2822},"Pydantic v2 у FastAPI — схеми, валідація та серіалізація","\u002Fpython\u002Ffastapi\u002Ffastapi-pydantic-schemas","05.python\u002Ffastapi\u002F19.fastapi-pydantic-schemas",{"title":2824,"path":2825,"stem":2826},"Dependency Injection — серце архітектури FastAPI","\u002Fpython\u002Ffastapi\u002Ffastapi-dependency-injection","05.python\u002Ffastapi\u002F20.fastapi-dependency-injection",{"title":2828,"path":2829,"stem":2830},"Обробка помилок, Middleware та CORS у FastAPI","\u002Fpython\u002Ffastapi\u002Ffastapi-errors-middleware","05.python\u002Ffastapi\u002F21.fastapi-errors-middleware",{"title":2832,"path":2833,"stem":2834},"SQLAlchemy 2.0 — ORM, Core та Async Engine","\u002Fpython\u002Ffastapi\u002Fsqlalchemy-orm","05.python\u002Ffastapi\u002F22.sqlalchemy-orm",{"title":2836,"path":2837,"stem":2838},"Alembic — міграції бази даних","\u002Fpython\u002Ffastapi\u002Falembic-migrations","05.python\u002Ffastapi\u002F23.alembic-migrations",{"title":2840,"path":2841,"stem":2842},"Repository Pattern та Clean Architecture у FastAPI","\u002Fpython\u002Ffastapi\u002Ffastapi-repository-clean-arch","05.python\u002Ffastapi\u002F24.fastapi-repository-clean-arch",{"title":2844,"path":2845,"stem":2846},"Автентифікація та авторизація — JWT, OAuth2, RBAC","\u002Fpython\u002Ffastapi\u002Ffastapi-auth-security","05.python\u002Ffastapi\u002F25.fastapi-auth-security",{"title":2848,"path":2849,"stem":2850},"Тестування FastAPI — pytest, httpx, fixtures","\u002Fpython\u002Ffastapi\u002Ffastapi-testing","05.python\u002Ffastapi\u002F26.fastapi-testing",{"title":2852,"path":2853,"stem":2854},"Async на повну — Background Tasks, WebSockets, SSE та Celery","\u002Fpython\u002Ffastapi\u002Ffastapi-async-advanced","05.python\u002Ffastapi\u002F27.fastapi-async-advanced",{"title":2856,"path":2857,"stem":2858},"Deployment — Docker, CI\u002FCD, Monitoring","\u002Fpython\u002Ffastapi\u002Ffastapi-deployment","05.python\u002Ffastapi\u002F28.fastapi-deployment",{"title":2860,"path":2861,"stem":2862},"[object Object]","\u002Fpython\u002Foop-plan","05.python\u002Foop-plan",{"title":2864,"icon":2865,"path":2866,"stem":2867,"children":2868,"page":59},"Бази даних","i-lucide-database","\u002Fdatabases","06.databases",[2869,2899,2922,2959,2988,3006,3040,3052,3061],{"title":2870,"icon":2871,"path":2872,"stem":2873,"children":2874,"page":59},"Intro","i-lucide-play","\u002Fdatabases\u002Fintro","06.databases\u002F01.intro",[2875,2879,2883,2887,2891,2895],{"title":2876,"path":2877,"stem":2878},"Введення в теорію баз даних","\u002Fdatabases\u002Fintro\u002Fintroduction-to-databases","06.databases\u002F01.intro\u002F01.introduction-to-databases",{"title":2880,"path":2881,"stem":2882},"Реляційна модель даних","\u002Fdatabases\u002Fintro\u002Frelational-model-theory","06.databases\u002F01.intro\u002F02.relational-model-theory",{"title":2884,"path":2885,"stem":2886},"ER-моделювання","\u002Fdatabases\u002Fintro\u002Fer-modeling","06.databases\u002F01.intro\u002F03.er-modeling",{"title":2888,"path":2889,"stem":2890},"Логічне проектування БД","\u002Fdatabases\u002Fintro\u002Flogical-schema","06.databases\u002F01.intro\u002F04.logical-schema",{"title":2892,"path":2893,"stem":2894},"Класифікація таблиць","\u002Fdatabases\u002Fintro\u002Ftable-classification","06.databases\u002F01.intro\u002F05.table-classification",{"title":2896,"path":2897,"stem":2898},"PlantUML для баз даних","\u002Fdatabases\u002Fintro\u002Fplantuml-diagrams","06.databases\u002F01.intro\u002F06.plantuml-diagrams",{"title":2900,"icon":2865,"path":2901,"stem":2902,"children":2903,"page":59},"MS SQL Server Start","\u002Fdatabases\u002Fms-sql-server-start","06.databases\u002F02.ms-sql-server-start",[2904,2908,2914,2918],{"title":2905,"path":2906,"stem":2907},"Типи даних у MS SQL Server","\u002Fdatabases\u002Fms-sql-server-start\u002Fdata-types","06.databases\u002F02.ms-sql-server-start\u002F01.data-types",{"title":2909,"path":2910,"stem":2911,"children":2912},"Індекси у MS SQL Server","\u002Fdatabases\u002Fms-sql-server-start\u002Fsql-indexes","06.databases\u002F02.ms-sql-server-start\u002F02.sql-indexes",[2913],{"title":2909,"path":2910,"stem":2911},{"title":2915,"path":2916,"stem":2917},"Системні бази даних MS SQL Server","\u002Fdatabases\u002Fms-sql-server-start\u002Fsystem-databases","06.databases\u002F02.ms-sql-server-start\u002F03.system-databases",{"title":2919,"path":2920,"stem":2921},"Огляд мови SQL та запитів","\u002Fdatabases\u002Fms-sql-server-start\u002Fsql-queries-overview","06.databases\u002F02.ms-sql-server-start\u002F04.sql-queries-overview",{"title":2923,"icon":2865,"path":2924,"stem":2925,"children":2926,"page":59},"SQL","\u002Fdatabases\u002Fsql","06.databases\u002F03.sql",[2927,2931,2935,2939,2943,2947,2951,2955],{"title":2928,"path":2929,"stem":2930},"Налаштування демонстраційної бази даних","\u002Fdatabases\u002Fsql\u002Fsample-database-setup","06.databases\u002F03.sql\u002F00.sample-database-setup",{"title":2932,"path":2933,"stem":2934},"DDL - Створення таблиць (CREATE TABLE)","\u002Fdatabases\u002Fsql\u002Fddl-create-table","06.databases\u002F03.sql\u002F01.ddl-create-table",{"title":2936,"path":2937,"stem":2938},"DDL - Зміна та видалення таблиць (ALTER, DROP)","\u002Fdatabases\u002Fsql\u002Fddl-alter-drop-table","06.databases\u002F03.sql\u002F02.ddl-alter-drop-table",{"title":2940,"path":2941,"stem":2942},"SELECT запити - Основи","\u002Fdatabases\u002Fsql\u002Fselect-queries-fundamentals","06.databases\u002F03.sql\u002F03.select-queries-fundamentals",{"title":2944,"path":2945,"stem":2946},"SELECT запити - Розширені можливості","\u002Fdatabases\u002Fsql\u002Fselect-queries-advanced","06.databases\u002F03.sql\u002F04.select-queries-advanced",{"title":2948,"path":2949,"stem":2950},"INSERT запити - Додавання даних","\u002Fdatabases\u002Fsql\u002Finsert-queries","06.databases\u002F03.sql\u002F05.insert-queries",{"title":2952,"path":2953,"stem":2954},"UPDATE та DELETE запити","\u002Fdatabases\u002Fsql\u002Fupdate-delete-queries","06.databases\u002F03.sql\u002F06.update-delete-queries",{"title":2956,"path":2957,"stem":2958},"Транзакції в SQL","\u002Fdatabases\u002Fsql\u002Ftransactions","06.databases\u002F03.sql\u002F07.transactions",{"title":2960,"icon":2865,"path":2961,"stem":2962,"children":2963,"page":59},"Multi Table Databases","\u002Fdatabases\u002Fmulti-table-databases","06.databases\u002F04.multi-table-databases",[2964,2968,2972,2976,2980,2984],{"title":2965,"path":2966,"stem":2967},"Зв'язки та нормалізація БД","\u002Fdatabases\u002Fmulti-table-databases\u002Frelationships-and-normalization","06.databases\u002F04.multi-table-databases\u002F00.relationships-and-normalization",{"title":2969,"path":2970,"stem":2971},"INNER JOIN - З'єднання таблиць","\u002Fdatabases\u002Fmulti-table-databases\u002Finner-join","06.databases\u002F04.multi-table-databases\u002F01.inner-join",{"title":2973,"path":2974,"stem":2975},"OUTER JOINs - LEFT, RIGHT, FULL","\u002Fdatabases\u002Fmulti-table-databases\u002Fouter-joins","06.databases\u002F04.multi-table-databases\u002F02.outer-joins",{"title":2977,"path":2978,"stem":2979},"CROSS та SELF JOINs","\u002Fdatabases\u002Fmulti-table-databases\u002Fcross-self-joins","06.databases\u002F04.multi-table-databases\u002F03.cross-self-joins",{"title":2981,"path":2982,"stem":2983},"Підзапити (Subqueries)","\u002Fdatabases\u002Fmulti-table-databases\u002Fsubqueries","06.databases\u002F04.multi-table-databases\u002F04.subqueries",{"title":2985,"path":2986,"stem":2987},"Агрегації з JOIN","\u002Fdatabases\u002Fmulti-table-databases\u002Faggregations-with-joins","06.databases\u002F04.multi-table-databases\u002F05.aggregations-with-joins",{"title":2989,"icon":2990,"path":2991,"stem":2992,"children":2993,"page":59},"Aggregate Functions","i-lucide-calculator","\u002Fdatabases\u002Faggregate-functions","06.databases\u002F05.aggregate-functions",[2994,2998,3002],{"title":2995,"path":2996,"stem":2997},"Функції агрегування в MS SQL Server","\u002Fdatabases\u002Faggregate-functions\u002Fintroduction-aggregate-functions","06.databases\u002F05.aggregate-functions\u002F01.introduction-aggregate-functions",{"title":2999,"path":3000,"stem":3001},"Групування даних в MS SQL Server","\u002Fdatabases\u002Faggregate-functions\u002Fgrouping-data","06.databases\u002F05.aggregate-functions\u002F02.grouping-data",{"title":3003,"path":3004,"stem":3005},"Підзапити з агрегатними функціями","\u002Fdatabases\u002Faggregate-functions\u002Fsubqueries-aggregates","06.databases\u002F05.aggregate-functions\u002F03.subqueries-aggregates",{"title":3007,"icon":3008,"path":3009,"stem":3010,"children":3011,"page":59},"Тригери та зберігаємі процедури","i-lucide-database-zap","\u002Fdatabases\u002Ftriggers-stored-procedures","06.databases\u002F07.triggers-stored-procedures",[3012,3016,3020,3024,3028,3032,3036],{"title":3013,"path":3014,"stem":3015},"DML-тригери","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Fdml-triggers","06.databases\u002F07.triggers-stored-procedures\u002F01.dml-triggers",{"title":3017,"path":3018,"stem":3019},"DDL-тригери","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Fddl-triggers","06.databases\u002F07.triggers-stored-procedures\u002F02.ddl-triggers",{"title":3021,"path":3022,"stem":3023},"Transact-SQL розширення","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Ftransact-sql-extensions","06.databases\u002F07.triggers-stored-procedures\u002F03.transact-sql-extensions",{"title":3025,"path":3026,"stem":3027},"Транзакції","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Ftransactions","06.databases\u002F07.triggers-stored-procedures\u002F04.transactions",{"title":3029,"path":3030,"stem":3031},"Зберігаємі процедури","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Fstored-procedures","06.databases\u002F07.triggers-stored-procedures\u002F05.stored-procedures",{"title":3033,"path":3034,"stem":3035},"Користувацькі функції","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Fuser-defined-functions","06.databases\u002F07.triggers-stored-procedures\u002F06.user-defined-functions",{"title":3037,"path":3038,"stem":3039},"Безпека баз даних","\u002Fdatabases\u002Ftriggers-stored-procedures\u002Fsecurity","06.databases\u002F07.triggers-stored-procedures\u002F08.security",{"title":3037,"icon":793,"path":3041,"stem":3042,"children":3043,"page":59},"\u002Fdatabases\u002Fsecurity","06.databases\u002F08.security",[3044,3048],{"title":3045,"path":3046,"stem":3047},"Вступ до безпеки баз даних","\u002Fdatabases\u002Fsecurity\u002Fintroduction","06.databases\u002F08.security\u002F01.introduction",{"title":3049,"path":3050,"stem":3051},"Системні представлення та метадані","\u002Fdatabases\u002Fsecurity\u002Fsystem-views","06.databases\u002F08.security\u002F02.system-views",{"title":3053,"icon":3054,"path":3055,"stem":3056,"children":3057,"page":59},"Резервне копіювання та відновлення","i-lucide-database-backup","\u002Fdatabases\u002Fbackup-recovery","06.databases\u002F09.backup-recovery",[3058],{"title":3053,"path":3059,"stem":3060},"\u002Fdatabases\u002Fbackup-recovery\u002Fbackup-restore","06.databases\u002F09.backup-recovery\u002F01.backup-restore",{"title":3062,"icon":3063,"path":3064,"stem":3065,"children":3066,"page":59},"Повнотекстовий пошук","i-lucide-search","\u002Fdatabases\u002Ffull-text-search","06.databases\u002F10.full-text-search",[3067],{"title":3062,"path":3068,"stem":3069},"\u002Fdatabases\u002Ffull-text-search\u002Ffull-text-search","06.databases\u002F10.full-text-search\u002F01.full-text-search",{"title":3071,"icon":3072,"path":3073,"stem":3074,"children":3075,"page":59},"Tools","i-lucide-wrench","\u002Ftools","07.tools",[3076,3152],{"title":3077,"icon":3078,"path":3079,"stem":3080,"children":3081},"Docker","i-simple-icons-docker","\u002Ftools\u002Fdocker","07.tools\u002F01.docker\u002Findex",[3082,3084,3088,3092,3096,3100,3104,3108,3112,3116,3120,3124,3128,3132,3136,3140,3144,3148],{"title":3083,"path":3079,"stem":3080},"Docker: від нуля до production",{"title":3085,"path":3086,"stem":3087},"Контейнеризація — від проблеми до рішення","\u002Ftools\u002Fdocker\u002Fcontainerization-concept","07.tools\u002F01.docker\u002F01.containerization-concept",{"title":3089,"path":3090,"stem":3091},"Docker — що це і навіщо?","\u002Ftools\u002Fdocker\u002Fdocker-what-and-why","07.tools\u002F01.docker\u002F02.docker-what-and-why",{"title":3093,"path":3094,"stem":3095},"Архітектура Docker Engine","\u002Ftools\u002Fdocker\u002Fdocker-architecture","07.tools\u002F01.docker\u002F03.docker-architecture",{"title":3097,"path":3098,"stem":3099},"Встановлення Docker","\u002Ftools\u002Fdocker\u002Finstallation","07.tools\u002F01.docker\u002F04.installation",{"title":3101,"path":3102,"stem":3103},"Перший контейнер — docker run","\u002Ftools\u002Fdocker\u002Ffirst-container","07.tools\u002F01.docker\u002F05.first-container",{"title":3105,"path":3106,"stem":3107},"Життєвий цикл контейнера","\u002Ftools\u002Fdocker\u002Fcontainer-lifecycle","07.tools\u002F01.docker\u002F06.container-lifecycle",{"title":3109,"path":3110,"stem":3111},"Docker Images — фундаментальні концепції","\u002Ftools\u002Fdocker\u002Fdocker-images-fundamentals","07.tools\u002F01.docker\u002F07.docker-images-fundamentals",{"title":3113,"path":3114,"stem":3115},"Dockerfile — основи","\u002Ftools\u002Fdocker\u002Fdockerfile-basics","07.tools\u002F01.docker\u002F08.dockerfile-basics",{"title":3117,"path":3118,"stem":3119},"Dockerfile — просунуті техніки","\u002Ftools\u002Fdocker\u002Fdockerfile-advanced","07.tools\u002F01.docker\u002F09.dockerfile-advanced",{"title":3121,"path":3122,"stem":3123},"Build Context та кешування шарів","\u002Ftools\u002Fdocker\u002Fbuild-context-and-cache","07.tools\u002F01.docker\u002F10.build-context-and-cache",{"title":3125,"path":3126,"stem":3127},"Реєстри Docker-образів","\u002Ftools\u002Fdocker\u002Fimage-registries","07.tools\u002F01.docker\u002F11.image-registries",{"title":3129,"path":3130,"stem":3131},"Контейнеризація .NET додатків","\u002Ftools\u002Fdocker\u002Fdotnet-containerization","07.tools\u002F01.docker\u002F12.dotnet-containerization",{"title":3133,"path":3134,"stem":3135},"Томи та збереження даних","\u002Ftools\u002Fdocker\u002Fvolumes-and-data","07.tools\u002F01.docker\u002F13.volumes-and-data",{"title":3137,"path":3138,"stem":3139},"Основи мережі в Docker","\u002Ftools\u002Fdocker\u002Fnetworking-basics","07.tools\u002F01.docker\u002F14.networking-basics",{"title":3141,"path":3142,"stem":3143},"Змінні оточення та конфігурація","\u002Ftools\u002Fdocker\u002Fenvironment-and-configuration","07.tools\u002F01.docker\u002F15.environment-and-configuration",{"title":3145,"path":3146,"stem":3147},"Docker Compose — оркестрація контейнерів","\u002Ftools\u002Fdocker\u002Fdocker-compose-basics","07.tools\u002F01.docker\u002F16.docker-compose-basics",{"title":3149,"path":3150,"stem":3151},"Docker Compose — Multi-Service застосунки","\u002Ftools\u002Fdocker\u002Fcompose-multi-service","07.tools\u002F01.docker\u002F17.compose-multi-service",{"title":3153,"icon":3154,"path":3155,"stem":3156,"children":3157},"Kubernetes","simple-icons:kubernetes","\u002Ftools\u002Fkubernetes","07.tools\u002F02.kubernetes\u002Findex",[3158,3160,3164,3168,3172,3176,3180,3184,3188],{"title":3159,"path":3155,"stem":3156},"Kubernetes: від розробки до production",{"title":3161,"path":3162,"stem":3163},"Kubernetes — коли Docker Compose більше не вистачає","\u002Ftools\u002Fkubernetes\u002Fwhy-kubernetes","07.tools\u002F02.kubernetes\u002F01.why-kubernetes",{"title":3165,"path":3166,"stem":3167},"Архітектура Kubernetes — анатомія кластера","\u002Ftools\u002Fkubernetes\u002Fkubernetes-architecture","07.tools\u002F02.kubernetes\u002F02.kubernetes-architecture",{"title":3169,"path":3170,"stem":3171},"Локальне середовище — minikube, kind та k3s","\u002Ftools\u002Fkubernetes\u002Flocal-environment","07.tools\u002F02.kubernetes\u002F03.local-environment",{"title":3173,"path":3174,"stem":3175},"Pod — атомарна одиниця Kubernetes","\u002Ftools\u002Fkubernetes\u002Fpods-and-containers","07.tools\u002F02.kubernetes\u002F04.pods-and-containers",{"title":3177,"path":3178,"stem":3179},"Патерни використання Pod","\u002Ftools\u002Fkubernetes\u002Fpod-patterns","07.tools\u002F02.kubernetes\u002F05.pod-patterns",{"title":3181,"path":3182,"stem":3183},"Deployment — декларативне управління Pod","\u002Ftools\u002Fkubernetes\u002Fdeployment-basics","07.tools\u002F02.kubernetes\u002F06.deployment-basics",{"title":3185,"path":3186,"stem":3187},"Rolling Updates та управління життєвим циклом Deployment","\u002Ftools\u002Fkubernetes\u002Fdeployment-rolling-updates","07.tools\u002F02.kubernetes\u002F07.deployment-rolling-updates",{"title":3189,"path":3190,"stem":3191},"Service — мережева абстракція для Pod","\u002Ftools\u002Fkubernetes\u002Fservices-networking","07.tools\u002F02.kubernetes\u002F08.services-networking",{"title":3193,"icon":3194,"path":3195,"stem":3196,"children":3197,"page":59},"Software Engineering","i-lucide-code-2","\u002Fsoftware-engineering","09.software-engineering",[3198,3202,3206,3210,3214,3218,3222,3226,3230,3234,3238],{"title":3199,"path":3200,"stem":3201},"1. Аналіз предметної області. Експертні знання та складність","\u002Fsoftware-engineering\u002Fintro-subdomains","09.software-engineering\u002F01.intro-subdomains",{"title":3203,"path":3204,"stem":3205},"2. Обмежені контексти. Інтеграція обмежених контекстів","\u002Fsoftware-engineering\u002Fintegrating-limited-contexts","09.software-engineering\u002F02.integrating-limited-contexts",{"title":3207,"path":3208,"stem":3209},"3. Реалізація простої бізнес-логіки","\u002Fsoftware-engineering\u002Fsimple","09.software-engineering\u002F03.simple",{"title":3211,"path":3212,"stem":3213},"4. Опрацювання складної бізнес-логіки","\u002Fsoftware-engineering\u002Fcomplex-business-logic","09.software-engineering\u002F04.complex-business-logic",{"title":3215,"path":3216,"stem":3217},"5. Моделювання фактора часу. Подієво-орієнтована архітектура.","\u002Fsoftware-engineering\u002Fmodelling-the-time-factor","09.software-engineering\u002F05.modelling-the-time-factor",{"title":3219,"path":3220,"stem":3221},"6. Архітектурні патерни","\u002Fsoftware-engineering\u002Farchitectural-patterns","09.software-engineering\u002F06.architectural-patterns",{"title":3223,"path":3224,"stem":3225},"Паттерни взаємодії","\u002Fsoftware-engineering\u002Fpatterns-of-interaction","09.software-engineering\u002F07.patterns-of-interaction",{"title":3227,"path":3228,"stem":3229},"Евристика проєктування","\u002Fsoftware-engineering\u002Fdesign-heuristics","09.software-engineering\u002F08.design-heuristics",{"title":3231,"path":3232,"stem":3233},"Еволюція проєктних рішень","\u002Fsoftware-engineering\u002Fevolution-of-design-solutions","09.software-engineering\u002F09.evolution-of-design-solutions",{"title":3235,"path":3236,"stem":3237},"EventStorming","\u002Fsoftware-engineering\u002Feventstorming","09.software-engineering\u002F10.eventstorming",{"title":3239,"path":3240,"stem":3241},"DDD на практиці","\u002Fsoftware-engineering\u002Fddd-in-practice","09.software-engineering\u002F11.ddd-in-practice",{"title":3243,"icon":943,"path":3244,"stem":3245,"children":3246,"page":59},"DDD","\u002Fddd","10.ddd",[3247,3251,3255,3259,3263,3267,3271,3275,3279,3283,3287,3291,3295],{"title":3248,"path":3249,"stem":3250},"Аналіз предметної області","\u002Fddd\u002Fdomain-analysis","10.ddd\u002F01.domain-analysis",{"title":3252,"path":3253,"stem":3254},"Експертні знання про предметну область","\u002Fddd\u002Fdomain-expert-knowledge","10.ddd\u002F02.domain-expert-knowledge",{"title":3256,"path":3257,"stem":3258},"Як осмислити складність предметної області","\u002Fddd\u002Fmanaging-domain-complexity","10.ddd\u002F03.managing-domain-complexity",{"title":3260,"path":3261,"stem":3262},"Інтеграція обмежених контекстів","\u002Fddd\u002Fbounded-context-integration","10.ddd\u002F04.bounded-context-integration",{"title":3264,"path":3265,"stem":3266},"Реалізація простої бізнес-логіки","\u002Fddd\u002Fsimple-business-logic","10.ddd\u002F05.simple-business-logic",{"title":3268,"path":3269,"stem":3270},"Обробка складної бізнес-логіки","\u002Fddd\u002Fcomplex-business-logic","10.ddd\u002F06.complex-business-logic",{"title":3272,"path":3273,"stem":3274},"Моделювання фактора часу","\u002Fddd\u002Ftime-modeling","10.ddd\u002F07.time-modeling",{"title":3276,"path":3277,"stem":3278},"Глава 8. Архітектурні Патерни","\u002Fddd\u002Farchitectural-patterns","10.ddd\u002F08.architectural-patterns",{"title":3280,"path":3281,"stem":3282},"Глава 9. Патерни Взаємодії","\u002Fddd\u002Finteraction-patterns","10.ddd\u002F09.interaction-patterns",{"title":3284,"path":3285,"stem":3286},"Глава 10. Проектні Евристики","\u002Fddd\u002Fdesign-heuristics","10.ddd\u002F10.design-heuristics",{"title":3288,"path":3289,"stem":3290},"Глава 11. Еволюція Проектних Рішень","\u002Fddd\u002Fevolution-of-design-decisions","10.ddd\u002F11.evolution-of-design-decisions",{"title":3292,"path":3293,"stem":3294},"Глава 12. EventStorming","\u002Fddd\u002Fevent-storming","10.ddd\u002F12.event-storming",{"title":3296,"path":3297,"stem":3298},"Глава 13. DDD на Практиці","\u002Fddd\u002Fddd-in-practice","10.ddd\u002F13.ddd-in-practice",{"title":3300,"icon":3301,"path":3302,"stem":3303,"children":3304,"page":59},"Media Streaming","i-lucide-video","\u002Fmedia-streaming","11.media-streaming",[3305,3309,3313,3317,3321,3325,3329],{"title":3306,"path":3307,"stem":3308},"01. Магія Стрімінгу: Що відбувається, коли ви натискаєте \"Play\"","\u002Fmedia-streaming\u002Fintroduction","11.media-streaming\u002F01.introduction",{"title":3310,"path":3311,"stem":3312},"02. Анатомія Медіа: Кодеки, Контейнери та Стиснення","\u002Fmedia-streaming\u002Faudio-video-anatomy","11.media-streaming\u002F02.audio-video-anatomy",{"title":3314,"path":3315,"stem":3316},"03. The Gym: FFmpeg Deep Dive","\u002Fmedia-streaming\u002Fffmpeg-gym","11.media-streaming\u002F03.ffmpeg-gym",{"title":3318,"path":3319,"stem":3320},"04. HLS Protocol: HTTP Live Streaming у Деталях","\u002Fmedia-streaming\u002Fhls-protocol","11.media-streaming\u002F04.hls-protocol",{"title":3322,"path":3323,"stem":3324},"05. DASH Protocol: Відкритий Стандарт","\u002Fmedia-streaming\u002Fdash-protocol","11.media-streaming\u002F05.dash-protocol",{"title":3326,"path":3327,"stem":3328},"06. Масштабування: CDN та Adaptive Bitrate","\u002Fmedia-streaming\u002Fcdn-and-adaptive-bitrate","11.media-streaming\u002F06.cdn-and-adaptive-bitrate",{"title":3330,"path":3331,"stem":3332},"07. Війна із Затримкою (Latency)","\u002Fmedia-streaming\u002Frealtime-latency","11.media-streaming\u002F07.realtime-latency",{"title":3334,"icon":3335,"path":3336,"stem":3337,"children":3338,"page":59},"HTML & CSS","i-devicon-html5","\u002Fhtml-css","12.html-css",[3339,3343,3347,3351,3355,3359,3363,3367,3371,3375,3379,3383,3387,3391,3395,3399,3403,3407,3411,3415,3419,3423,3427,3431,3435,3439,3443,3447,3451,3455],{"title":3340,"path":3341,"stem":3342},"Вступ до HTML. Структура документа","\u002Fhtml-css\u002Fintro-html-structure","12.html-css\u002F01.intro-html-structure",{"title":3344,"path":3345,"stem":3346},"Форматування тексту в HTML","\u002Fhtml-css\u002Fhtml-text-formatting","12.html-css\u002F02.html-text-formatting",{"title":3348,"path":3349,"stem":3350},"Посилання та зображення в HTML","\u002Fhtml-css\u002Fhtml-links-images","12.html-css\u002F03.html-links-images",{"title":3352,"path":3353,"stem":3354},"Списки та таблиці в HTML","\u002Fhtml-css\u002Fhtml-lists-tables","12.html-css\u002F04.html-lists-tables",{"title":3356,"path":3357,"stem":3358},"Форми в HTML","\u002Fhtml-css\u002Fhtml-forms","12.html-css\u002F05.html-forms",{"title":3360,"path":3361,"stem":3362},"Семантичні елементи HTML5","\u002Fhtml-css\u002Fhtml-semantic-elements","12.html-css\u002F06.html-semantic-elements",{"title":3364,"path":3365,"stem":3366},"Мультимедіа та розширені елементи HTML","\u002Fhtml-css\u002Fhtml-multimedia-advanced","12.html-css\u002F07.html-multimedia-advanced",{"title":3368,"path":3369,"stem":3370},"Мікророзмітка та SEO в HTML","\u002Fhtml-css\u002Fhtml-microdata-seo","12.html-css\u002F08.html-microdata-seo",{"title":3372,"path":3373,"stem":3374},"Вступ до CSS. Селектори та специфічність","\u002Fhtml-css\u002Fcss-intro-selectors","12.html-css\u002F09.css-intro-selectors",{"title":3376,"path":3377,"stem":3378},"Блокова модель CSS. Відступи. Box Sizing","\u002Fhtml-css\u002Fcss-box-model","12.html-css\u002F10.css-box-model",{"title":3380,"path":3381,"stem":3382},"Розміри у CSS: повний довідник одиниць і ключових слів","\u002Fhtml-css\u002F10a.css-sizing","12.html-css\u002F10a.css-sizing",{"title":3384,"path":3385,"stem":3386},"Типографіка в CSS. Шрифти та текст","\u002Fhtml-css\u002Fcss-typography","12.html-css\u002F11.css-typography",{"title":3388,"path":3389,"stem":3390},"Кольори та фони в CSS","\u002Fhtml-css\u002Fcss-colors-backgrounds","12.html-css\u002F12.css-colors-backgrounds",{"title":3392,"path":3393,"stem":3394},"Тіні та фільтри в CSS","\u002Fhtml-css\u002F12b.css-shadows-filters","12.html-css\u002F12b.css-shadows-filters",{"title":3396,"path":3397,"stem":3398},"CSS Flexbox: Фундамент гнучких макетів","\u002Fhtml-css\u002Fcss-flexbox-fundamentals","12.html-css\u002F13.css-flexbox-fundamentals",{"title":3400,"path":3401,"stem":3402},"CSS Flexbox: Вирівнювання та Позиціонування","\u002Fhtml-css\u002Fcss-flexbox-alignment-sizing-and-patterns","12.html-css\u002F14.css-flexbox-alignment-sizing-and-patterns",{"title":3404,"path":3405,"stem":3406},"CSS Grid. Двовимірний макет. Частина 1","\u002Fhtml-css\u002Fcss-layout-grid","12.html-css\u002F15.css-layout-grid",{"title":3408,"path":3409,"stem":3410},"CSS Grid. Двовимірний макет. Частина 2","\u002Fhtml-css\u002Fcss-layout-grid-advanced","12.html-css\u002F16.css-layout-grid-advanced",{"title":3412,"path":3413,"stem":3414},"Позиціонування в CSS. Z-index. Stacking Context","\u002Fhtml-css\u002Fcss-positioning","12.html-css\u002F17.css-positioning",{"title":3416,"path":3417,"stem":3418},"CSS Анімації та Переходи","\u002Fhtml-css\u002Fcss-animations-transitions","12.html-css\u002F18.css-animations-transitions",{"title":3420,"path":3421,"stem":3422},"Адаптивний дизайн. Media Queries. Частина 1","\u002Fhtml-css\u002Fcss-responsive-media-queries","12.html-css\u002F19.css-responsive-media-queries",{"title":3424,"path":3425,"stem":3426},"Адаптивний дизайн. Частина 2: clamp(), Container Queries, @layer","\u002Fhtml-css\u002Fcss-responsive-advanced","12.html-css\u002F20.css-responsive-advanced",{"title":3428,"path":3429,"stem":3430},"CSS Custom Properties. Методології. Сучасний CSS","\u002Fhtml-css\u002Fcss-variables-methodologies","12.html-css\u002F21.css-variables-methodologies",{"title":3432,"path":3433,"stem":3434},"Сучасний CSS 2023–2025: Нові можливості","\u002Fhtml-css\u002Fcss-modern-features","12.html-css\u002F22.css-modern-features",{"title":3436,"path":3437,"stem":3438},"CSS Nesting, @layer, @scope та @property: нативний препроцесор","\u002Fhtml-css\u002F22a.css-nesting-modern-syntax","12.html-css\u002F22a.css-nesting-modern-syntax",{"title":3440,"path":3441,"stem":3442},"CSS для форм та інтерактивних станів","\u002Fhtml-css\u002Fcss-forms-interactive-states","12.html-css\u002F23.css-forms-interactive-states",{"title":3444,"path":3445,"stem":3446},"Доступність у CSS (CSS Accessibility)","\u002Fhtml-css\u002Fcss-accessibility","12.html-css\u002F24.css-accessibility",{"title":3448,"path":3449,"stem":3450},"CSS-функції та сучасні sizing primitives","\u002Fhtml-css\u002Fcss-functions-sizing","12.html-css\u002F25.css-functions-sizing",{"title":3452,"path":3453,"stem":3454},"Rendering Pipeline і CSS Performance","\u002Fhtml-css\u002Fcss-rendering-performance","12.html-css\u002F26.css-rendering-performance",{"title":3456,"path":3457,"stem":3458},"CSS Best Practices: типові ситуації та правильні рішення","\u002Fhtml-css\u002Fcss-best-practices","12.html-css\u002F27.css-best-practices",{"title":3460,"path":3461,"stem":3462,"children":3463,"page":59},"AWS","\u002Faws","13.aws",[3464,3468,3472,3476,3480,3484,3488,3492,3496,3500,3504,3508,3512,3516,3520,3524,3528,3532],{"title":3465,"path":3466,"stem":3467},"Реєстрація AWS акаунту та студентські програми","\u002Faws\u002Faccount-registration","13.aws\u002F00.account-registration",{"title":3469,"path":3470,"stem":3471},"Вступ до хмарних обчислень та AWS","\u002Faws\u002Fintroduction-to-cloud","13.aws\u002F01.introduction-to-cloud",{"title":3473,"path":3474,"stem":3475},"AWS IAM — Identity and Access Management","\u002Faws\u002Fiam","13.aws\u002F02.iam",{"title":3477,"path":3478,"stem":3479},"AWS IAM CLI — Довідник команд","\u002Faws\u002F02a.iam-doc","13.aws\u002F02a.iam-doc",{"title":3481,"path":3482,"stem":3483},"Docker та контейнеризація в AWS — ECR, ECS та Fargate","\u002Faws\u002Fdocker-ecs","13.aws\u002F03.docker-ecs",{"title":3485,"path":3486,"stem":3487},"AWS ECR \u002F ECS CLI — Довідник команд","\u002Faws\u002F03a.docker-ecs-doc","13.aws\u002F03a.docker-ecs-doc",{"title":3489,"path":3490,"stem":3491},"Amazon EC2 — Elastic Compute Cloud","\u002Faws\u002Fec2","13.aws\u002F04.ec2",{"title":3493,"path":3494,"stem":3495},"AWS EC2 CLI — Довідник команд","\u002Faws\u002F04a.ec2-doc","13.aws\u002F04a.ec2-doc",{"title":3497,"path":3498,"stem":3499},"Elastic Load Balancing та Auto Scaling","\u002Faws\u002Falb-asg","13.aws\u002F05.alb-asg",{"title":3501,"path":3502,"stem":3503},"Amazon S3 — Simple Storage Service","\u002Faws\u002Fs3","13.aws\u002F06.s3",{"title":3505,"path":3506,"stem":3507},"Amazon CloudFront — Content Delivery Network","\u002Faws\u002Fcloudfront","13.aws\u002F07.cloudfront",{"title":3509,"path":3510,"stem":3511},"Amazon RDS — Relational Database Service","\u002Faws\u002Frds","13.aws\u002F08.rds",{"title":3513,"path":3514,"stem":3515},"Amazon DynamoDB — NoSQL Database","\u002Faws\u002Fdynamodb","13.aws\u002F09.dynamodb",{"title":3517,"path":3518,"stem":3519},"AWS Lambda та Serverless Compute","\u002Faws\u002Flambda","13.aws\u002F10.lambda",{"title":3521,"path":3522,"stem":3523},"Amazon Bedrock - Foundation Models, RAG та Agents","\u002Faws\u002Fbedrock","13.aws\u002F22.bedrock",{"title":3525,"path":3526,"stem":3527},"Amazon Rekognition - Комп'ютерний зір","\u002Faws\u002Frekognition","13.aws\u002F23.rekognition",{"title":3529,"path":3530,"stem":3531},"Amazon Textract - Інтелектуальний аналіз документів","\u002Faws\u002Ftextract","13.aws\u002F24.textract",{"title":3533,"path":3534,"stem":3535},"Amazon Polly, Transcribe, Comprehend та Translate","\u002Faws\u002Faudio-nlp-services","13.aws\u002F25.audio-nlp-services",{"title":3537,"path":3538,"stem":3539,"children":3540,"page":59},"Ai Development","\u002Fai-development","14.ai-development",[3541],{"title":3542,"path":3543,"stem":3544,"children":3545,"page":59},"Foundations","\u002Fai-development\u002Ffoundations","14.ai-development\u002F00.foundations",[3546],{"title":3547,"path":3548,"stem":3549},"Модуль 0.1. Що таке Large Language Model (LLM)","\u002Fai-development\u002Ffoundations\u002Fwhat-is-llm","14.ai-development\u002F00.foundations\u002F01.what-is-llm",{"title":3551,"path":3552,"stem":3553,"children":3554,"page":59},"React Native","\u002Freact-native","15.react-native",[3555,3576],{"title":3556,"path":3557,"stem":3558,"children":3559,"page":59},"Version1","\u002Freact-native\u002Fversion1","15.react-native\u002Fversion1",[3560,3564,3568,3572],{"title":3561,"path":3562,"stem":3563},"Що таке React Native","\u002Freact-native\u002Fversion1\u002Fwhat-is-react-native","15.react-native\u002Fversion1\u002F01.what-is-react-native",{"title":3565,"path":3566,"stem":3567},"Як влаштований React Native «під капотом»","\u002Freact-native\u002Fversion1\u002Freact-native-architecture","15.react-native\u002Fversion1\u002F02.react-native-architecture",{"title":3569,"path":3570,"stem":3571},"Від React до React Native: ключові відмінності","\u002Freact-native\u002Fversion1\u002Freact-native-vs-react","15.react-native\u002Fversion1\u002F03.react-native-vs-react",{"title":3573,"path":3574,"stem":3575},"View та Text — фундаментальні будівельні блоки","\u002Freact-native\u002Fversion1\u002Fview-and-text","15.react-native\u002Fversion1\u002F04.view-and-text",{"title":3577,"path":3578,"stem":3579,"children":3580,"page":59},"Version2","\u002Freact-native\u002Fversion2","15.react-native\u002Fversion2",[3581,3585,3589,3593,3597,3601,3605,3609,3613,3617,3621,3625,3629,3633,3637,3641,3645,3649,3653,3657,3661,3665,3669,3673,3677,3681,3685],{"title":3582,"path":3583,"stem":3584},"React Native — навіщо він потрібен","\u002Freact-native\u002Fversion2\u002Fwhy-react-native","15.react-native\u002Fversion2\u002F01.why-react-native",{"title":3586,"path":3587,"stem":3588},"Налаштування середовища розробки","\u002Freact-native\u002Fversion2\u002Fenvironment-setup","15.react-native\u002Fversion2\u002F02.environment-setup",{"title":3590,"path":3591,"stem":3592},"Expo і класичний React Native — два шляхи одного фреймворку","\u002Freact-native\u002Fversion2\u002Fexpo-vs-cli-landscape","15.react-native\u002Fversion2\u002F03.expo-vs-cli-landscape",{"title":3594,"path":3595,"stem":3596},"Середовище Expo — перший запуск","\u002Freact-native\u002Fversion2\u002Fexpo-setup-and-tooling","15.react-native\u002Fversion2\u002F04.expo-setup-and-tooling",{"title":3598,"path":3599,"stem":3600},"Структура проєкту та перші UI-примітиви","\u002Freact-native\u002Fversion2\u002Fproject-structure-and-conventions","15.react-native\u002Fversion2\u002F05.project-structure-and-conventions",{"title":3602,"path":3603,"stem":3604},"Базові компоненти React Native","\u002Freact-native\u002Fversion2\u002Fcore-components","15.react-native\u002Fversion2\u002F06.core-components",{"title":3606,"path":3607,"stem":3608},"Flexbox і layout у React Native","\u002Freact-native\u002Fversion2\u002Fflexbox-and-layout","15.react-native\u002Fversion2\u002F07.flexbox-and-layout",{"title":3610,"path":3611,"stem":3612},"StyleSheet і теми (light \u002F dark)","\u002Freact-native\u002Fversion2\u002Fstylesheet-and-theming","15.react-native\u002Fversion2\u002F08.stylesheet-and-theming",{"title":3614,"path":3615,"stem":3616},"Списки та віртуалізація","\u002Freact-native\u002Fversion2\u002Flists-and-virtualization","15.react-native\u002Fversion2\u002F09.lists-and-virtualization",{"title":3618,"path":3619,"stem":3620},"Форми, ввід і валідація","\u002Freact-native\u002Fversion2\u002Fforms-input-validation","15.react-native\u002Fversion2\u002F10.forms-input-validation",{"title":3622,"path":3623,"stem":3624},"Основи Expo Router","\u002Freact-native\u002Fversion2\u002Fexpo-router-basics","15.react-native\u002Fversion2\u002F11.expo-router-basics",{"title":3626,"path":3627,"stem":3628},"Вкладені навігатори та модалки","\u002Freact-native\u002Fversion2\u002Fnested-navigators-and-modals","15.react-native\u002Fversion2\u002F12.nested-navigators-and-modals",{"title":3630,"path":3631,"stem":3632},"Deep linking і параметри маршрутів","\u002Freact-native\u002Fversion2\u002Fdeep-linking-and-params","15.react-native\u002Fversion2\u002F13.deep-linking-and-params",{"title":3634,"path":3635,"stem":3636},"Робота з мережею та життєвий цикл застосунку","\u002Freact-native\u002Fversion2\u002Fnetworking-and-app-lifecycle","15.react-native\u002Fversion2\u002F14.networking-and-app-lifecycle",{"title":3638,"path":3639,"stem":3640},"Redux Toolkit на мобільних платформах","\u002Freact-native\u002Fversion2\u002Fredux-toolkit-on-mobile","15.react-native\u002Fversion2\u002F15.redux-toolkit-on-mobile",{"title":3642,"path":3643,"stem":3644},"RTK Query на мобільних платформах","\u002Freact-native\u002Fversion2\u002Frtk-query-mobile","15.react-native\u002Fversion2\u002F16.rtk-query-mobile",{"title":3646,"path":3647,"stem":3648},"Автентифікація та Безпечне Зберігання","\u002Freact-native\u002Fversion2\u002Fauth-secure-storage","15.react-native\u002Fversion2\u002F18.auth-secure-storage",{"title":3650,"path":3651,"stem":3652},"Локальне сховище — AsyncStorage, MMKV, SQLite","\u002Freact-native\u002Fversion2\u002Flocal-storage-mmkv-sqlite","15.react-native\u002Fversion2\u002F19.local-storage-mmkv-sqlite",{"title":3654,"path":3655,"stem":3656},"Офлайн-First і синхронізація","\u002Freact-native\u002Fversion2\u002Foffline-first-sync","15.react-native\u002Fversion2\u002F20.offline-first-sync",{"title":3658,"path":3659,"stem":3660},"Камера, медіа та файлова система","\u002Freact-native\u002Fversion2\u002Fcamera-media-filesystem","15.react-native\u002Fversion2\u002F21.camera-media-filesystem",{"title":3662,"path":3663,"stem":3664},"Карти та геолокація","\u002Freact-native\u002Fversion2\u002Fmaps-and-location","15.react-native\u002Fversion2\u002F22.maps-and-location",{"title":3666,"path":3667,"stem":3668},"Жести та дотики","\u002Freact-native\u002Fversion2\u002Fgestures-and-touch","15.react-native\u002Fversion2\u002F23.gestures-and-touch",{"title":3670,"path":3671,"stem":3672},"Анімації з Reanimated","\u002Freact-native\u002Fversion2\u002Freanimated-animations","15.react-native\u002Fversion2\u002F24.reanimated-animations",{"title":3674,"path":3675,"stem":3676},"Push і локальні сповіщення","\u002Freact-native\u002Fversion2\u002Fpush-and-local-notifications","15.react-native\u002Fversion2\u002F25.push-and-local-notifications",{"title":3678,"path":3679,"stem":3680},"Інтернаціоналізація та доступність","\u002Freact-native\u002Fversion2\u002Faccessibility-and-i18n","15.react-native\u002Fversion2\u002F26.accessibility-and-i18n",{"title":3682,"path":3683,"stem":3684},"EAS Build та профілі збірки","\u002Freact-native\u002Fversion2\u002Feas-build-and-profiles","15.react-native\u002Fversion2\u002F29.eas-build-and-profiles",{"title":3686,"path":3687,"stem":3688},"Публікація в App Store та Google Play","\u002Freact-native\u002Fversion2\u002Fapp-store-play-release","15.react-native\u002Fversion2\u002F30.app-store-play-release",{"title":3690,"path":3691,"stem":3692,"children":3693,"page":59},"Ai Python","\u002Fai-python","16.ai-python",[3694,3703,3712,3721,3750,3776,3801,3834,3851,3864,3873,3886,3895,3916,3929,3950],{"title":3695,"path":3696,"stem":3697,"children":3698,"page":59},"Intro To Ai","\u002Fai-python\u002Fintro-to-ai","16.ai-python\u002F01.intro-to-ai",[3699],{"title":3700,"path":3701,"stem":3702},"Вступ до штучного інтелекту та машинного навчання","\u002Fai-python\u002Fintro-to-ai\u002Fintro-to-ai","16.ai-python\u002F01.intro-to-ai\u002F01.intro-to-ai",{"title":3704,"path":3705,"stem":3706,"children":3707,"page":59},"Jupyter Notebook","\u002Fai-python\u002Fjupyter-notebook","16.ai-python\u002F02.jupyter-notebook",[3708],{"title":3709,"path":3710,"stem":3711},"Знайомство з Jupyter Notebook та JupyterLab","\u002Fai-python\u002Fjupyter-notebook\u002Fjupyter-notebook","16.ai-python\u002F02.jupyter-notebook\u002F02.jupyter-notebook",{"title":3713,"path":3714,"stem":3715,"children":3716,"page":59},"Numpy Pandas","\u002Fai-python\u002Fnumpy-pandas","16.ai-python\u002F03.numpy-pandas",[3717],{"title":3718,"path":3719,"stem":3720},"Робота з даними з використанням NumPy і pandas","\u002Fai-python\u002Fnumpy-pandas\u002Fnumpy-pandas","16.ai-python\u002F03.numpy-pandas\u002F03.numpy-pandas",{"title":3722,"path":3723,"stem":3724,"children":3725,"page":59},"Statistics Basics","\u002Fai-python\u002Fstatistics-basics","16.ai-python\u002F04.statistics-basics",[3726,3730,3734,3738,3742,3746],{"title":3727,"path":3728,"stem":3729},"Випадкові величини та їх типи","\u002Fai-python\u002Fstatistics-basics\u002Frandom-variables","16.ai-python\u002F04.statistics-basics\u002F01.random-variables",{"title":3731,"path":3732,"stem":3733},"Міри центральної тенденції","\u002Fai-python\u002Fstatistics-basics\u002Fcentral-tendency","16.ai-python\u002F04.statistics-basics\u002F02.central-tendency",{"title":3735,"path":3736,"stem":3737},"Міри варіації (розсіювання)","\u002Fai-python\u002Fstatistics-basics\u002Fvariability","16.ai-python\u002F04.statistics-basics\u002F03.variability",{"title":3739,"path":3740,"stem":3741},"Розподіли випадкових величин","\u002Fai-python\u002Fstatistics-basics\u002Fdistributions","16.ai-python\u002F04.statistics-basics\u002F04.distributions",{"title":3743,"path":3744,"stem":3745},"Кореляція та взаємозв'язки","\u002Fai-python\u002Fstatistics-basics\u002Fcorrelation","16.ai-python\u002F04.statistics-basics\u002F05.correlation",{"title":3747,"path":3748,"stem":3749},"Викиди та аномалії","\u002Fai-python\u002Fstatistics-basics\u002Foutliers","16.ai-python\u002F04.statistics-basics\u002F06.outliers",{"title":3751,"path":3752,"stem":3753,"children":3754},"Візуалізація даних за допомогою matplotlib","\u002Fai-python\u002Fdata-visualization","16.ai-python\u002F05.data-visualization\u002Findex",[3755,3756,3760,3764,3768,3772],{"title":3751,"path":3752,"stem":3753},{"title":3757,"path":3758,"stem":3759},"Основи matplotlib — Figure, Axes та перший графік","\u002Fai-python\u002Fdata-visualization\u002Fmatplotlib-basics","16.ai-python\u002F05.data-visualization\u002F01.matplotlib-basics",{"title":3761,"path":3762,"stem":3763},"Візуалізація розподілів — гістограми та box plots","\u002Fai-python\u002Fdata-visualization\u002Fdistributions","16.ai-python\u002F05.data-visualization\u002F02.distributions",{"title":3765,"path":3766,"stem":3767},"Візуалізація залежностей — scatter plots та кореляція","\u002Fai-python\u002Fdata-visualization\u002Frelationships","16.ai-python\u002F05.data-visualization\u002F03.relationships",{"title":3769,"path":3770,"stem":3771},"Візуалізація категоріальних даних — bar charts та pie charts","\u002Fai-python\u002Fdata-visualization\u002Fcategorical","16.ai-python\u002F05.data-visualization\u002F04.categorical",{"title":3773,"path":3774,"stem":3775},"Композиції та стилізація — subplots та багатопанельні дашборди","\u002Fai-python\u002Fdata-visualization\u002Fsubplots-styling","16.ai-python\u002F05.data-visualization\u002F05.subplots-styling",{"title":3777,"path":3778,"stem":3779,"children":3780,"page":59},"Linear Regression","\u002Fai-python\u002Flinear-regression","16.ai-python\u002F06.linear-regression",[3781,3785,3789,3793,3797],{"title":3782,"path":3783,"stem":3784},"Регресійна задача та лінійна регресія з однією змінною","\u002Fai-python\u002Flinear-regression\u002Fregression-task","16.ai-python\u002F06.linear-regression\u002F01.regression-task",{"title":3786,"path":3787,"stem":3788},"Метрики якості регресії — як оцінити модель","\u002Fai-python\u002Flinear-regression\u002Fquality-metrics","16.ai-python\u002F06.linear-regression\u002F02.quality-metrics",{"title":3790,"path":3791,"stem":3792},"Градієнтний спуск — як модель навчається","\u002Fai-python\u002Flinear-regression\u002Fgradient-descent","16.ai-python\u002F06.linear-regression\u002F03.gradient-descent",{"title":3794,"path":3795,"stem":3796},"Множинна лінійна регресія — модель з багатьма ознаками","\u002Fai-python\u002Flinear-regression\u002Fmultiple-regression","16.ai-python\u002F06.linear-regression\u002F04.multiple-regression",{"title":3798,"path":3799,"stem":3800},"Практичні завдання та резюме модуля","\u002Fai-python\u002Flinear-regression\u002Fpractice","16.ai-python\u002F06.linear-regression\u002F05.practice",{"title":3802,"path":3803,"stem":3804,"children":3805,"page":59},"Practical Regression","\u002Fai-python\u002Fpractical-regression","16.ai-python\u002F07.practical-regression",[3806,3810,3814,3818,3822,3826,3830],{"title":3807,"path":3808,"stem":3809},"Знайомство з scikit-learn","\u002Fai-python\u002Fpractical-regression\u002Fintro-scikit-learn","16.ai-python\u002F07.practical-regression\u002F01.intro-scikit-learn",{"title":3811,"path":3812,"stem":3813},"Exploratory Data Analysis датасету Housing Prices","\u002Fai-python\u002Fpractical-regression\u002Fhousing-dataset-eda","16.ai-python\u002F07.practical-regression\u002F02.housing-dataset-eda",{"title":3815,"path":3816,"stem":3817},"Попередня обробка даних (Data Preprocessing)","\u002Fai-python\u002Fpractical-regression\u002Fdata-preprocessing","16.ai-python\u002F07.practical-regression\u002F03.data-preprocessing",{"title":3819,"path":3820,"stem":3821},"Train\u002FTest Split та перша повноцінна модель","\u002Fai-python\u002Fpractical-regression\u002Ftrain-test-split","16.ai-python\u002F07.practical-regression\u002F04.train-test-split",{"title":3823,"path":3824,"stem":3825},"Перенавчання та недонавчання","\u002Fai-python\u002Fpractical-regression\u002Foverfitting-underfitting","16.ai-python\u002F07.practical-regression\u002F05.overfitting-underfitting",{"title":3827,"path":3828,"stem":3829},"Крос-валідація (Cross-Validation)","\u002Fai-python\u002Fpractical-regression\u002Fcross-validation","16.ai-python\u002F07.practical-regression\u002F06.cross-validation",{"title":3831,"path":3832,"stem":3833},"Гіперпараметри та їх оптимізація","\u002Fai-python\u002Fpractical-regression\u002Fhyperparameter-tuning","16.ai-python\u002F07.practical-regression\u002F07.hyperparameter-tuning",{"title":3835,"path":3836,"stem":3837,"children":3838,"page":59},"Polynomial Regression","\u002Fai-python\u002Fpolynomial-regression","16.ai-python\u002F08.polynomial-regression",[3839,3843,3847],{"title":3840,"path":3841,"stem":3842},"Нелінійні залежності та поліноміальна регресія","\u002Fai-python\u002Fpolynomial-regression\u002Fnonlinear-patterns","16.ai-python\u002F08.polynomial-regression\u002F01.nonlinear-patterns",{"title":3844,"path":3845,"stem":3846},"Регуляризація в регресії — Ridge та Lasso","\u002Fai-python\u002Fpolynomial-regression\u002Fregularization-methods","16.ai-python\u002F08.polynomial-regression\u002F02.regularization-methods",{"title":3848,"path":3849,"stem":3850},"Практика — поліноміальна регресія та регуляризація на Housing Prices","\u002Fai-python\u002Fpolynomial-regression\u002Fhousing-prices-practice","16.ai-python\u002F08.polynomial-regression\u002F03.housing-prices-practice",{"title":3852,"path":3853,"stem":3854,"children":3855,"page":59},"Decision Trees Random Forest","\u002Fai-python\u002Fdecision-trees-random-forest","16.ai-python\u002F09.decision-trees-random-forest",[3856,3860],{"title":3857,"path":3858,"stem":3859},"Дерева рішень — моделі з прозорою та зрозумілою логікою","\u002Fai-python\u002Fdecision-trees-random-forest\u002Fdecision-trees","16.ai-python\u002F09.decision-trees-random-forest\u002F01.decision-trees",{"title":3861,"path":3862,"stem":3863},"Random Forest — ансамблі дерев рішень","\u002Fai-python\u002Fdecision-trees-random-forest\u002Frandom-forest","16.ai-python\u002F09.decision-trees-random-forest\u002F02.random-forest",{"title":3865,"path":3866,"stem":3867,"children":3868,"page":59},"Gradient Boosting","\u002Fai-python\u002Fgradient-boosting","16.ai-python\u002F10.gradient-boosting",[3869],{"title":3870,"path":3871,"stem":3872},"Градієнтний бустинг та XGBoost — послідовне виправлення помилок","\u002Fai-python\u002Fgradient-boosting\u002Fgradient-boosting-xgboost","16.ai-python\u002F10.gradient-boosting\u002F01.gradient-boosting-xgboost",{"title":3874,"path":3875,"stem":3876,"children":3877,"page":59},"Logistic Regression Classification","\u002Fai-python\u002Flogistic-regression-classification","16.ai-python\u002F11.logistic-regression-classification",[3878,3882],{"title":3879,"path":3880,"stem":3881},"Основи класифікації та логістична регресія","\u002Fai-python\u002Flogistic-regression-classification\u002Fclassification-basics","16.ai-python\u002F11.logistic-regression-classification\u002F01.classification-basics",{"title":3883,"path":3884,"stem":3885},"Метрики якості класифікації","\u002Fai-python\u002Flogistic-regression-classification\u002Fclassification-metrics","16.ai-python\u002F11.logistic-regression-classification\u002F02.classification-metrics",{"title":3887,"path":3888,"stem":3889,"children":3890,"page":59},"Neural Networks Basics","\u002Fai-python\u002Fneural-networks-basics","16.ai-python\u002F12.neural-networks-basics",[3891],{"title":3892,"path":3893,"stem":3894},"Вступ до нейронних мереж — від персептрона до багатошарових мереж","\u002Fai-python\u002Fneural-networks-basics\u002Fperceptron-to-mlp","16.ai-python\u002F12.neural-networks-basics\u002F01.perceptron-to-mlp",{"title":3896,"path":3897,"stem":3898,"children":3899,"page":59},"Pytorch Intro","\u002Fai-python\u002Fpytorch-intro","16.ai-python\u002F13.pytorch-intro",[3900,3904,3908,3912],{"title":3901,"path":3902,"stem":3903},"Тензори та автоматичне диференціювання в PyTorch","\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd","16.ai-python\u002F13.pytorch-intro\u002F01.tensors-and-autograd",{"title":3905,"path":3906,"stem":3907},"Побудова та тренування нейронних мереж у PyTorch","\u002Fai-python\u002Fpytorch-intro\u002Fbuilding-and-training","16.ai-python\u002F13.pytorch-intro\u002F02.building-and-training",{"title":3909,"path":3910,"stem":3911},"Робота з даними та регуляризація в PyTorch","\u002Fai-python\u002Fpytorch-intro\u002Fdata-and-regularization","16.ai-python\u002F13.pytorch-intro\u002F03.data-and-regularization",{"title":3913,"path":3914,"stem":3915},"Просунуті теми PyTorch — GPU, оптимізація та deployment","\u002Fai-python\u002Fpytorch-intro\u002Fadvanced-topics","16.ai-python\u002F13.pytorch-intro\u002F04.advanced-topics",{"title":3917,"path":3918,"stem":3919,"children":3920,"page":59},"Activation Functions Heart Disease","\u002Fai-python\u002Factivation-functions-heart-disease","16.ai-python\u002F14.activation-functions-heart-disease",[3921,3925],{"title":3922,"path":3923,"stem":3924},"Функції активації та побудова нейронної мережі для передбачення вад серця","\u002Fai-python\u002Factivation-functions-heart-disease\u002Fheart-disease-prediction","16.ai-python\u002F14.activation-functions-heart-disease\u002F01.heart-disease-prediction",{"title":3926,"path":3927,"stem":3928},"Бонус — Повний приклад Student Performance Prediction","\u002Fai-python\u002Factivation-functions-heart-disease\u002Fstudent-performance-bonus","16.ai-python\u002F14.activation-functions-heart-disease\u002F02.student-performance-bonus",{"title":3930,"path":3931,"stem":3932,"children":3933,"page":59},"Cnn Image Classification","\u002Fai-python\u002Fcnn-image-classification","16.ai-python\u002F15.cnn-image-classification",[3934,3938,3942,3946],{"title":3935,"path":3936,"stem":3937},"Основи згорткових нейронних мереж — чому не MLP?","\u002Fai-python\u002Fcnn-image-classification\u002Fcnn-fundamentals","16.ai-python\u002F15.cnn-image-classification\u002F01.cnn-fundamentals",{"title":3939,"path":3940,"stem":3941},"Практична імплементація CNN для класифікації MNIST","\u002Fai-python\u002Fcnn-image-classification\u002Fmnist-classification","16.ai-python\u002F15.cnn-image-classification\u002F02.mnist-classification",{"title":3943,"path":3944,"stem":3945},"Глибші CNN та аугментація даних для CIFAR-10","\u002Fai-python\u002Fcnn-image-classification\u002Fcifar10-and-data-augmentation","16.ai-python\u002F15.cnn-image-classification\u002F03.cifar10-and-data-augmentation",{"title":3947,"path":3948,"stem":3949},"Transfer Learning та візуалізація внутрішніх представлень CNN","\u002Fai-python\u002Fcnn-image-classification\u002Ftransfer-learning-and-visualization","16.ai-python\u002F15.cnn-image-classification\u002F04.transfer-learning-and-visualization",{"title":3951,"path":3952,"stem":3953,"children":3954,"page":59},"Batching Normalization Deep Networks","\u002Fai-python\u002Fbatching-normalization-deep-networks","16.ai-python\u002F16.batching-normalization-deep-networks",[3955,3959,3963,3967,3971,3975,3979],{"title":3956,"path":3957,"stem":3958},"Mini-batch Gradient Descent та ефективна обробка даних","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Fmini-batch-gradient-descent","16.ai-python\u002F16.batching-normalization-deep-networks\u002F01.mini-batch-gradient-descent",{"title":3960,"path":3961,"stem":3962},"Ініціалізація ваг — фундамент стабільного навчання","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Fweight-initialization","16.ai-python\u002F16.batching-normalization-deep-networks\u002F02.weight-initialization",{"title":3964,"path":3965,"stem":3966},"Batch Normalization — прорив у глибокому навчанні","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Fbatch-normalization","16.ai-python\u002F16.batching-normalization-deep-networks\u002F03.batch-normalization",{"title":3968,"path":3969,"stem":3970},"Dropout та комбіновані стратегії регуляризації","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Fdropout-and-regularization","16.ai-python\u002F16.batching-normalization-deep-networks\u002F04.dropout-and-regularization",{"title":3972,"path":3973,"stem":3974},"Проєктування глибоких мереж — від теорії до практики","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Fdeep-networks-architectures","16.ai-python\u002F16.batching-normalization-deep-networks\u002F05.deep-networks-architectures",{"title":3976,"path":3977,"stem":3978},"Планування learning rate — коли та як зменшувати крок навчання","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Flearning-rate-scheduling","16.ai-python\u002F16.batching-normalization-deep-networks\u002F06.learning-rate-scheduling",{"title":3980,"path":3981,"stem":3982},"Підсумковий проєкт — побудова сучасного класифікатора зображень","\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Ffinal-project-deep-classifier","16.ai-python\u002F16.batching-normalization-deep-networks\u002F07.final-project-deep-classifier",{"title":3984,"icon":658,"path":3985,"stem":3986,"children":3987,"page":59},"Клієнт-серверна архітектура","\u002Fclient-server-architecture","17.client-server-architecture",[3988,3992,3996,4000,4004,4008,4012,4016,4020,4024,4028,4032,4036,4040,4044,4048,4052,4056],{"title":3989,"path":3990,"stem":3991},"Архітектура клієнт-серверних систем та основи Go","\u002Fclient-server-architecture\u002Fclient-server-architecture-and-go","17.client-server-architecture\u002F01.client-server-architecture-and-go",{"title":3993,"path":3994,"stem":3995},"Система типів та модель пам’яті мови Go","\u002Fclient-server-architecture\u002Ftype-system-and-memory-model","17.client-server-architecture\u002F02.type-system-and-memory-model",{"title":3997,"path":3998,"stem":3999},"Інтерфейси та модель конкурентності мови Go","\u002Fclient-server-architecture\u002Finterfaces-and-concurrency-model","17.client-server-architecture\u002F03.interfaces-and-concurrency-model",{"title":4001,"path":4002,"stem":4003},"Сокетне програмування та протокол TCP у Go","\u002Fclient-server-architecture\u002Ftcp-socket-programming","17.client-server-architecture\u002F04.tcp-socket-programming",{"title":4005,"path":4006,"stem":4007},"Протокол UDP та датаграмна передача даних","\u002Fclient-server-architecture\u002Fudp-datagram-transmission","17.client-server-architecture\u002F05.udp-datagram-transmission",{"title":4009,"path":4010,"stem":4011},"Криптографічний захист каналів зв","\u002Fclient-server-architecture\u002Ftls-cryptographic-channel-protection","17.client-server-architecture\u002F06.tls-cryptographic-channel-protection",{"title":4013,"path":4014,"stem":4015},"Прикладні мережеві протоколи DNS та SMTP","\u002Fclient-server-architecture\u002Fdns-smtp-application-protocols","17.client-server-architecture\u002F07.dns-smtp-application-protocols",{"title":4017,"path":4018,"stem":4019},"Протокол HTTP та стандартний вебсервер Go","\u002Fclient-server-architecture\u002Fhttp-protocol-and-web-server","17.client-server-architecture\u002F08.http-protocol-and-web-server",{"title":4021,"path":4022,"stem":4023},"Проєктування REST API та маршрутизація засобами Chi","\u002Fclient-server-architecture\u002Frest-api-design-and-chi-routing","17.client-server-architecture\u002F09.rest-api-design-and-chi-routing",{"title":4025,"path":4026,"stem":4027},"Лекція 10. Шарувата архітектура та конвеєри обробки запитів","\u002Fclient-server-architecture\u002Flayered-architecture-and-middleware-pipelines","17.client-server-architecture\u002F10.layered-architecture-and-middleware-pipelines",{"title":4029,"path":4030,"stem":4031},"Лекція 11. Архітектура та маршрутизація фреймворку Gin","\u002Fclient-server-architecture\u002Fgin-framework-architecture-and-routing","17.client-server-architecture\u002F11.gin-framework-architecture-and-routing",{"title":4033,"path":4034,"stem":4035},"Лекція 12. Валідація даних, безпека та документування у Gin","\u002Fclient-server-architecture\u002Fdata-validation-security-and-documentation-in-gin","17.client-server-architecture\u002F12.data-validation-security-and-documentation-in-gin",{"title":4037,"path":4038,"stem":4039},"Лекція 13. Взаємодія з реляційними СУБД та технології ORM","\u002Fclient-server-architecture\u002Frelational-databases-and-orm","17.client-server-architecture\u002F13.relational-databases-and-orm",{"title":4041,"path":4042,"stem":4043},"Потокова передача даних та протокол WebSocket","\u002Fclient-server-architecture\u002Fstreaming-data-and-websockets","17.client-server-architecture\u002F14.streaming-data-and-websockets",{"title":4045,"path":4046,"stem":4047},"Міжсервісна взаємодія на основі протоколу gRPC","\u002Fclient-server-architecture\u002Fgrpc-inter-service-communication","17.client-server-architecture\u002F15.grpc-inter-service-communication",{"title":4049,"path":4050,"stem":4051},"Асинхронний обмін повідомленнями та протоколи черг","\u002Fclient-server-architecture\u002Fasynchronous-message-exchange-and-queue-protocols","17.client-server-architecture\u002F16.asynchronous-message-exchange-and-queue-protocols",{"title":4053,"path":4054,"stem":4055},"Протоколи потокового медіа та технологія WebRTC","\u002Fclient-server-architecture\u002Fstreaming-media-protocols-and-webrtc","17.client-server-architecture\u002F17.streaming-media-protocols-and-webrtc",{"title":4057,"path":4058,"stem":4059},"Моніторинг, безпека та контейнеризація застосунків","\u002Fclient-server-architecture\u002Fmonitoring-security-and-containerization","17.client-server-architecture\u002F18.monitoring-security-and-containerization",{"title":4061,"icon":2118,"path":4062,"stem":4063,"children":4064,"page":59},"Розробка WEB-застосувань","\u002Fweb-development","18.web-development",[4065,4110,4148,4177,4206,4263,4313,4407,4473,4555,4588,4616,4658,4704,4747],{"title":4066,"icon":658,"path":4067,"stem":4068,"children":4069,"page":59},"Архітектура комп'ютерних мереж та мережеві протоколи","\u002Fweb-development\u002Fnetwork-architecture-and-protocols","18.web-development\u002F01.network-architecture-and-protocols",[4070,4074,4078,4082,4086,4090,4094,4098,4102,4106],{"title":4071,"path":4072,"stem":4073},"Еволюція веброзробки та модель клієнт-сервер","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fweb-evolution-client-server","18.web-development\u002F01.network-architecture-and-protocols\u002F01.web-evolution-client-server",{"title":4075,"path":4076,"stem":4077},"Еталонна модель OSI","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fosi-model","18.web-development\u002F01.network-architecture-and-protocols\u002F02.osi-model",{"title":4079,"path":4080,"stem":4081},"Стек протоколів TCP\u002FIP","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Ftcp-ip-stack","18.web-development\u002F01.network-architecture-and-protocols\u002F03.tcp-ip-stack",{"title":4083,"path":4084,"stem":4085},"IP-адресація, порти та сокети","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fip-addressing-ports-sockets","18.web-development\u002F01.network-architecture-and-protocols\u002F04.ip-addressing-ports-sockets",{"title":4087,"path":4088,"stem":4089},"Протоколи TCP та UDP","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Ftcp-udp-protocols","18.web-development\u002F01.network-architecture-and-protocols\u002F05.tcp-udp-protocols",{"title":4091,"path":4092,"stem":4093},"Система доменних імен DNS","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fdns-system","18.web-development\u002F01.network-architecture-and-protocols\u002F06.dns-system",{"title":4095,"path":4096,"stem":4097},"Протокол SMTP та поштові протоколи","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fsmtp-email-protocols","18.web-development\u002F01.network-architecture-and-protocols\u002F07.smtp-email-protocols",{"title":4099,"path":4100,"stem":4101},"Протокол WebSocket","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fwebsocket-protocol","18.web-development\u002F01.network-architecture-and-protocols\u002F08.websocket-protocol",{"title":4103,"path":4104,"stem":4105},"Протокол SSH","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fssh-protocol","18.web-development\u002F01.network-architecture-and-protocols\u002F09.ssh-protocol",{"title":4107,"path":4108,"stem":4109},"Огляд сучасних серверних технологій","\u002Fweb-development\u002Fnetwork-architecture-and-protocols\u002Fmodern-backend-technologies","18.web-development\u002F01.network-architecture-and-protocols\u002F10.modern-backend-technologies",{"title":4111,"icon":4112,"path":4113,"stem":4114,"children":4115,"page":59},"Протокол HTTP та захищена передача даних HTTPS","i-lucide-lock","\u002Fweb-development\u002Fhttp-and-https","18.web-development\u002F02.http-and-https",[4116,4120,4124,4128,4132,4136,4140,4144],{"title":4117,"path":4118,"stem":4119},"Вступ до протоколу HTTP та еволюція веб-технологій","\u002Fweb-development\u002Fhttp-and-https\u002Fintroduction-and-evolution","18.web-development\u002F02.http-and-https\u002F01.introduction-and-evolution",{"title":4121,"path":4122,"stem":4123},"Анатомія HTTP-запиту","\u002Fweb-development\u002Fhttp-and-https\u002Fhttp-request-structure","18.web-development\u002F02.http-and-https\u002F02.http-request-structure",{"title":4125,"path":4126,"stem":4127},"Анатомія HTTP-відповіді","\u002Fweb-development\u002Fhttp-and-https\u002Fhttp-response-structure","18.web-development\u002F02.http-and-https\u002F03.http-response-structure",{"title":4129,"path":4130,"stem":4131},"Методи HTTP та їхня семантика","\u002Fweb-development\u002Fhttp-and-https\u002Fhttp-methods","18.web-development\u002F02.http-and-https\u002F04.http-methods",{"title":4133,"path":4134,"stem":4135},"Коди статусу HTTP та їхнє призначення","\u002Fweb-development\u002Fhttp-and-https\u002Fhttp-status-codes","18.web-development\u002F02.http-and-https\u002F05.http-status-codes",{"title":4137,"path":4138,"stem":4139},"HTTP-заголовки та їхнє призначення","\u002Fweb-development\u002Fhttp-and-https\u002Fhttp-headers","18.web-development\u002F02.http-and-https\u002F06.http-headers",{"title":4141,"path":4142,"stem":4143},"CORS та механізми безпеки HTTP","\u002Fweb-development\u002Fhttp-and-https\u002Fcors-and-security","18.web-development\u002F02.http-and-https\u002F07.cors-and-security",{"title":4145,"path":4146,"stem":4147},"HTTPS та криптографічні основи TLS","\u002Fweb-development\u002Fhttp-and-https\u002Fhttps-and-tls","18.web-development\u002F02.http-and-https\u002F08.https-and-tls",{"title":4149,"icon":2498,"path":4150,"stem":4151,"children":4152,"page":59},"Система типів мови TypeScript","\u002Fweb-development\u002Ftypescript-type-system","18.web-development\u002F03.typescript-type-system",[4153,4157,4161,4165,4169,4173],{"title":4154,"path":4155,"stem":4156},"Знайомство з TypeScript Compiler","\u002Fweb-development\u002Ftypescript-type-system\u002Fintroduction-compiler-config","18.web-development\u002F03.typescript-type-system\u002F01.introduction-compiler-config",{"title":4158,"path":4159,"stem":4160},"Примітивні типи даних: string, number, boolean","\u002Fweb-development\u002Ftypescript-type-system\u002Fprimitive-special-types","18.web-development\u002F03.typescript-type-system\u002F02.primitive-special-types",{"title":4162,"path":4163,"stem":4164},"Масиви, кортежі та об","\u002Fweb-development\u002Ftypescript-type-system\u002Farrays-tuples-objects","18.web-development\u002F03.typescript-type-system\u002F03.arrays-tuples-objects",{"title":4166,"path":4167,"stem":4168},"Псевдоніми типів, Union типи, Intersection типи та літеральні типи","\u002Fweb-development\u002Ftypescript-type-system\u002Funion-intersection-literals","18.web-development\u002F03.typescript-type-system\u002F04.union-intersection-literals",{"title":4170,"path":4171,"stem":4172},"Type Narrowing (звуження типів)","\u002Fweb-development\u002Ftypescript-type-system\u002Ftype-narrowing","18.web-development\u002F03.typescript-type-system\u002F05.type-narrowing",{"title":4174,"path":4175,"stem":4176},"Типізація функцій","\u002Fweb-development\u002Ftypescript-type-system\u002Ffunctions-typing","18.web-development\u002F03.typescript-type-system\u002F06.functions-typing",{"title":4178,"icon":2498,"path":4179,"stem":4180,"children":4181,"page":59},"Розширені засоби мови TypeScript","\u002Fweb-development\u002Ftypescript-advanced","18.web-development\u002F04.typescript-advanced",[4182,4186,4190,4194,4198,4202],{"title":4183,"path":4184,"stem":4185},"Interfaces та Classes","\u002Fweb-development\u002Ftypescript-advanced\u002Finterfaces-classes","18.web-development\u002F04.typescript-advanced\u002F01.interfaces-classes",{"title":4187,"path":4188,"stem":4189},"Основи Generic типів","\u002Fweb-development\u002Ftypescript-advanced\u002Fgenerics-fundamentals","18.web-development\u002F04.typescript-advanced\u002F02.generics-fundamentals",{"title":4191,"path":4192,"stem":4193},"Enums та Decorators","\u002Fweb-development\u002Ftypescript-advanced\u002Fenums-decorators","18.web-development\u002F04.typescript-advanced\u002F03.enums-decorators",{"title":4195,"path":4196,"stem":4197},"Utility Types","\u002Fweb-development\u002Ftypescript-advanced\u002Futility-types","18.web-development\u002F04.typescript-advanced\u002F04.utility-types",{"title":4199,"path":4200,"stem":4201},"Mapped та Conditional Types","\u002Fweb-development\u002Ftypescript-advanced\u002Fmapped-conditional-types","18.web-development\u002F04.typescript-advanced\u002F05.mapped-conditional-types",{"title":4203,"path":4204,"stem":4205},"Модулі та Strict Mode","\u002Fweb-development\u002Ftypescript-advanced\u002Fmodules-strict-mode","18.web-development\u002F04.typescript-advanced\u002F06.modules-strict-mode",{"title":4207,"icon":4208,"path":4209,"stem":4210,"children":4211,"page":59},"Платформа Node.js як середовище виконання","i-devicon-nodejs","\u002Fweb-development\u002Fnodejs-platform","18.web-development\u002F05.nodejs-platform",[4212,4216,4220,4224,4228,4232,4236,4240,4244,4248,4252,4255,4259],{"title":4213,"path":4214,"stem":4215},"Що таке Node.js та його архітектура","\u002Fweb-development\u002Fnodejs-platform\u002Fwhat-is-nodejs","18.web-development\u002F05.nodejs-platform\u002F01.what-is-nodejs",{"title":4217,"path":4218,"stem":4219},"Event Loop — серце Node.js","\u002Fweb-development\u002Fnodejs-platform\u002Fevent-loop","18.web-development\u002F05.nodejs-platform\u002F02.event-loop",{"title":4221,"path":4222,"stem":4223},"Модульні системи: CommonJS vs ES Modules","\u002Fweb-development\u002Fnodejs-platform\u002Fmodule-systems","18.web-development\u002F05.nodejs-platform\u002F03.module-systems",{"title":4225,"path":4226,"stem":4227},"npm та package.json","\u002Fweb-development\u002Fnodejs-platform\u002Fnpm-package-json","18.web-development\u002F05.nodejs-platform\u002F04.npm-package-json",{"title":4229,"path":4230,"stem":4231},"Глобальні об\\","\u002Fweb-development\u002Fnodejs-platform\u002Fglobal-objects","18.web-development\u002F05.nodejs-platform\u002F05.global-objects",{"title":4233,"path":4234,"stem":4235},"Вбудований модуль fs (File System)","\u002Fweb-development\u002Fnodejs-platform\u002Fbuilt-in-modules-fs","18.web-development\u002F05.nodejs-platform\u002F06.built-in-modules-fs",{"title":4237,"path":4238,"stem":4239},"Вбудований модуль path","\u002Fweb-development\u002Fnodejs-platform\u002Fbuilt-in-modules-path","18.web-development\u002F05.nodejs-platform\u002F07.built-in-modules-path",{"title":4241,"path":4242,"stem":4243},"Вбудований модуль http","\u002Fweb-development\u002Fnodejs-platform\u002Fbuilt-in-modules-http","18.web-development\u002F05.nodejs-platform\u002F08.built-in-modules-http",{"title":4245,"path":4246,"stem":4247},"Streams — потоки даних у Node.js","\u002Fweb-development\u002Fnodejs-platform\u002Fstreams","18.web-development\u002F05.nodejs-platform\u002F09.streams",{"title":4249,"path":4250,"stem":4251},"Вбудований модуль util (Utilities)","\u002Fweb-development\u002Fnodejs-platform\u002Fbuilt-in-modules-util","18.web-development\u002F05.nodejs-platform\u002F10.built-in-modules-util",{"title":3141,"path":4253,"stem":4254},"\u002Fweb-development\u002Fnodejs-platform\u002Fenvironment-variables","18.web-development\u002F05.nodejs-platform\u002F11.environment-variables",{"title":4256,"path":4257,"stem":4258},"Інструменти налагодження та розробки","\u002Fweb-development\u002Fnodejs-platform\u002Fdebugging-tools","18.web-development\u002F05.nodejs-platform\u002F12.debugging-tools",{"title":4260,"path":4261,"stem":4262},"Продуктивність та Best Practices","\u002Fweb-development\u002Fnodejs-platform\u002Fperformance-best-practices","18.web-development\u002F05.nodejs-platform\u002F13.performance-best-practices",{"title":4264,"icon":4265,"path":4266,"stem":4267,"children":4268,"page":59},"Асинхронне програмування у Node.js","i-lucide-clock","\u002Fweb-development\u002Fasync-programming-nodejs","18.web-development\u002F06.async-programming-nodejs",[4269,4273,4277,4281,4285,4289,4293,4297,4301,4305,4309],{"title":4270,"path":4271,"stem":4272},"Синхронний vs асинхронний код","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fsync-vs-async","18.web-development\u002F06.async-programming-nodejs\u002F01.sync-vs-async",{"title":4274,"path":4275,"stem":4276},"Callbacks — перша парадигма асинхронності","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fcallbacks","18.web-development\u002F06.async-programming-nodejs\u002F02.callbacks",{"title":4278,"path":4279,"stem":4280},"Promises — стандарт асинхронності","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fpromises","18.web-development\u002F06.async-programming-nodejs\u002F03.promises",{"title":4282,"path":4283,"stem":4284},"Promise комбінатори","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fpromise-combinators","18.web-development\u002F06.async-programming-nodejs\u002F04.promise-combinators",{"title":4286,"path":4287,"stem":4288},"Async\u002FAwait — синтаксичний цукор над Promises","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fasync-await","18.web-development\u002F06.async-programming-nodejs\u002F05.async-await",{"title":4290,"path":4291,"stem":4292},"Паралельне vs послідовне виконання","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fparallel-vs-sequential","18.web-development\u002F06.async-programming-nodejs\u002F06.parallel-vs-sequential",{"title":4294,"path":4295,"stem":4296},"EventEmitter — подієва модель Node.js","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fevent-emitter","18.web-development\u002F06.async-programming-nodejs\u002F07.event-emitter",{"title":4298,"path":4299,"stem":4300},"Worker Threads — багатопотоковість у Node.js","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fworker-threads","18.web-development\u002F06.async-programming-nodejs\u002F08.worker-threads",{"title":4302,"path":4303,"stem":4304},"Child Processes — запуск зовнішніх процесів","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fchild-processes","18.web-development\u002F06.async-programming-nodejs\u002F09.child-processes",{"title":4306,"path":4307,"stem":4308},"Обробка помилок у асинхронному коді","\u002Fweb-development\u002Fasync-programming-nodejs\u002Ferror-handling","18.web-development\u002F06.async-programming-nodejs\u002F10.error-handling",{"title":4310,"path":4311,"stem":4312},"Порівняння Node.js фреймворків","\u002Fweb-development\u002Fasync-programming-nodejs\u002Fnodejs-frameworks","18.web-development\u002F06.async-programming-nodejs\u002F11.nodejs-frameworks",{"title":4314,"icon":4315,"path":4316,"stem":4317,"children":4318,"page":59},"Тема 7. Фреймворк NestJS. Контролери та маршрутизація","i-lucide-route","\u002Fweb-development\u002Fnestjs-controllers-routing","18.web-development\u002F07.nestjs-controllers-routing",[4319,4323,4327,4331,4335,4339,4343,4347,4351,4355,4359,4363,4367,4371,4375,4379,4383,4387,4391,4395,4399,4403],{"title":4320,"path":4321,"stem":4322},"Філософія та архітектура NestJS","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fnestjs-philosophy","18.web-development\u002F07.nestjs-controllers-routing\u002F01.nestjs-philosophy",{"title":4324,"path":4325,"stem":4326},"Три стовпи архітектури: модулі, контролери, провайдери","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fthree-pillars","18.web-development\u002F07.nestjs-controllers-routing\u002F02.three-pillars",{"title":4328,"path":4329,"stem":4330},"Інверсія керування (IoC) та ін","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fioc-and-di","18.web-development\u002F07.nestjs-controllers-routing\u002F03.ioc-and-di",{"title":4332,"path":4333,"stem":4334},"Встановлення NestJS CLI та створення проєкту","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fnestjs-cli-setup","18.web-development\u002F07.nestjs-controllers-routing\u002F04.nestjs-cli-setup",{"title":4336,"path":4337,"stem":4338},"Структура проєкту NestJS та конвенції","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fproject-structure","18.web-development\u002F07.nestjs-controllers-routing\u002F05.project-structure",{"title":4340,"path":4341,"stem":4342},"Контролери: основи маршрутизації","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fcontrollers-basics","18.web-development\u002F07.nestjs-controllers-routing\u002F06.controllers-basics",{"title":4344,"path":4345,"stem":4346},"Декоратори маршрутизації HTTP-методів","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Froute-decorators","18.web-development\u002F07.nestjs-controllers-routing\u002F07.route-decorators",{"title":4348,"path":4349,"stem":4350},"Параметри маршруту та декоратор @Param","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Froute-parameters","18.web-development\u002F07.nestjs-controllers-routing\u002F08.route-parameters",{"title":4352,"path":4353,"stem":4354},"Query-параметри та декоратор @Query","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fquery-parameters","18.web-development\u002F07.nestjs-controllers-routing\u002F09.query-parameters",{"title":4356,"path":4357,"stem":4358},"Тіло запиту та декоратор @Body","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Frequest-body","18.web-development\u002F07.nestjs-controllers-routing\u002F10.request-body",{"title":4360,"path":4361,"stem":4362},"Робота з заголовками запиту та відповіді","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fheaders-decorators","18.web-development\u002F07.nestjs-controllers-routing\u002F11.headers-decorators",{"title":4364,"path":4365,"stem":4366},"Data Transfer Objects (DTO): визначення та призначення","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fdto-introduction","18.web-development\u002F07.nestjs-controllers-routing\u002F12.dto-introduction",{"title":4368,"path":4369,"stem":4370},"Об'єкти Request та Response","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Frequest-response-objects","18.web-development\u002F07.nestjs-controllers-routing\u002F13.request-response-objects",{"title":4372,"path":4373,"stem":4374},"HTTP-статус коди та декоратор @HttpCode","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fhttp-status-codes","18.web-development\u002F07.nestjs-controllers-routing\u002F14.http-status-codes",{"title":4376,"path":4377,"stem":4378},"Перенаправлення (Redirects) та декоратор @Redirect","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fredirects","18.web-development\u002F07.nestjs-controllers-routing\u002F15.redirects",{"title":4380,"path":4381,"stem":4382},"Асинхронні обробники та Observables","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fasync-handlers","18.web-development\u002F07.nestjs-controllers-routing\u002F16.async-handlers",{"title":4384,"path":4385,"stem":4386},"Маршрутизація за піддоменами та версіонування API","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fsub-domain-routing","18.web-development\u002F07.nestjs-controllers-routing\u002F17.sub-domain-routing",{"title":4388,"path":4389,"stem":4390},"Області видимості контролерів (Scopes)","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fcontroller-scopes","18.web-development\u002F07.nestjs-controllers-routing\u002F18.controller-scopes",{"title":4392,"path":4393,"stem":4394},"Основні команди NestJS CLI","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fnestjs-cli-commands","18.web-development\u002F07.nestjs-controllers-routing\u002F19.nestjs-cli-commands",{"title":4396,"path":4397,"stem":4398},"Практичний приклад: створення CRUD-контролера","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fcrud-example","18.web-development\u002F07.nestjs-controllers-routing\u002F20.crud-example",{"title":4400,"path":4401,"stem":4402},"Тестування контролерів","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Ftesting-controllers","18.web-development\u002F07.nestjs-controllers-routing\u002F21.testing-controllers",{"title":4404,"path":4405,"stem":4406},"Best Practices для контролерів та маршрутизації","\u002Fweb-development\u002Fnestjs-controllers-routing\u002Fbest-practices","18.web-development\u002F07.nestjs-controllers-routing\u002F22.best-practices",{"title":4408,"icon":4409,"path":4410,"stem":4411,"children":4412,"page":59},"Тема 8. Провайдери, сервіси та модульна система NestJS","i-lucide-package","\u002Fweb-development\u002Fnestjs-providers-modules","18.web-development\u002F08.nestjs-providers-modules",[4413,4417,4421,4425,4429,4433,4437,4441,4445,4449,4453,4457,4461,4465,4469],{"title":4414,"path":4415,"stem":4416},"Концепція провайдерів у NestJS","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fproviders-concept","18.web-development\u002F08.nestjs-providers-modules\u002F01.providers-concept",{"title":4418,"path":4419,"stem":4420},"Сервіси: інкапсуляція бізнес-логіки","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fservices-business-logic","18.web-development\u002F08.nestjs-providers-modules\u002F02.services-business-logic",{"title":4422,"path":4423,"stem":4424},"Практика ін","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fdependency-injection-practice","18.web-development\u002F08.nestjs-providers-modules\u002F03.dependency-injection-practice",{"title":4426,"path":4427,"stem":4428},"Custom Providers: useValue, useClass, useFactory, useExisting","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fcustom-providers","18.web-development\u002F08.nestjs-providers-modules\u002F04.custom-providers",{"title":4430,"path":4431,"stem":4432},"Injection Tokens: рядкові та символьні токени","\u002Fweb-development\u002Fnestjs-providers-modules\u002Finjection-tokens","18.web-development\u002F08.nestjs-providers-modules\u002F05.injection-tokens",{"title":4434,"path":4435,"stem":4436},"Опціональні провайдери та декоратор @Optional","\u002Fweb-development\u002Fnestjs-providers-modules\u002Foptional-providers","18.web-development\u002F08.nestjs-providers-modules\u002F07.optional-providers",{"title":4438,"path":4439,"stem":4440},"Структура модулів: imports, controllers, providers, exports","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fmodules-structure","18.web-development\u002F08.nestjs-providers-modules\u002F08.modules-structure",{"title":4442,"path":4443,"stem":4444},"Feature Modules: ізоляція функціональності","\u002Fweb-development\u002Fnestjs-providers-modules\u002Ffeature-modules","18.web-development\u002F08.nestjs-providers-modules\u002F09.feature-modules",{"title":4446,"path":4447,"stem":4448},"Shared Modules: перевикористання провайдерів","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fshared-modules","18.web-development\u002F08.nestjs-providers-modules\u002F10.shared-modules",{"title":4450,"path":4451,"stem":4452},"Global Modules та декоратор @Global","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fglobal-modules","18.web-development\u002F08.nestjs-providers-modules\u002F11.global-modules",{"title":4454,"path":4455,"stem":4456},"Dynamic Modules: конфігурація під час виконання","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fdynamic-modules","18.web-development\u002F08.nestjs-providers-modules\u002F12.dynamic-modules",{"title":4458,"path":4459,"stem":4460},"Циклічні залежності та forwardRef","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fcircular-dependencies","18.web-development\u002F08.nestjs-providers-modules\u002F13.circular-dependencies",{"title":4462,"path":4463,"stem":4464},"Lifecycle Hooks: хуки життєвого циклу","\u002Fweb-development\u002Fnestjs-providers-modules\u002Flifecycle-hooks","18.web-development\u002F08.nestjs-providers-modules\u002F14.lifecycle-hooks",{"title":4466,"path":4467,"stem":4468},"ModuleRef: динамічний доступ до провайдерів","\u002Fweb-development\u002Fnestjs-providers-modules\u002Fmodule-reference","18.web-development\u002F08.nestjs-providers-modules\u002F15.module-reference",{"title":4470,"path":4471,"stem":4472},"Тестування сервісів та провайдерів","\u002Fweb-development\u002Fnestjs-providers-modules\u002Ftesting-services","18.web-development\u002F08.nestjs-providers-modules\u002F16.testing-services",{"title":4474,"icon":4475,"path":4476,"stem":4477,"children":4478,"page":59},"Тема 9. Валідація даних та конвеєр обробки запитів у NestJS","i-lucide-filter","\u002Fweb-development\u002Fnestjs-validation-pipeline","18.web-development\u002F09.nestjs-validation-pipeline",[4479,4483,4487,4491,4495,4499,4503,4507,4511,4515,4519,4523,4527,4531,4535,4539,4543,4547,4551],{"title":4480,"path":4481,"stem":4482},"Request Pipeline: конвеєр обробки запитів","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Frequest-pipeline","18.web-development\u002F09.nestjs-validation-pipeline\u002F01.request-pipeline",{"title":4484,"path":4485,"stem":4486},"Pipes: концепція трансформації та валідації","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fpipes-concept","18.web-development\u002F09.nestjs-validation-pipeline\u002F02.pipes-concept",{"title":4488,"path":4489,"stem":4490},"Вбудовані pipes: ParseIntPipe, ParseBoolPipe, ParseUUIDPipe","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fbuilt-in-pipes","18.web-development\u002F09.nestjs-validation-pipeline\u002F03.built-in-pipes",{"title":4492,"path":4493,"stem":4494},"ValidationPipe: автоматична валідація DTO","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fvalidation-pipe","18.web-development\u002F09.nestjs-validation-pipeline\u002F04.validation-pipe",{"title":4496,"path":4497,"stem":4498},"Декоратори class-validator для DTO","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fclass-validator-decorators","18.web-development\u002F09.nestjs-validation-pipeline\u002F05.class-validator-decorators",{"title":4500,"path":4501,"stem":4502},"Створення кастомних валідаторів","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fcustom-validators","18.web-development\u002F09.nestjs-validation-pipeline\u002F06.custom-validators",{"title":4504,"path":4505,"stem":4506},"Створення власних Pipes","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fcustom-pipes","18.web-development\u002F09.nestjs-validation-pipeline\u002F07.custom-pipes",{"title":4508,"path":4509,"stem":4510},"Основи Middleware","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fmiddleware-basics","18.web-development\u002F09.nestjs-validation-pipeline\u002F08.middleware-basics",{"title":4512,"path":4513,"stem":4514},"Практичні приклади Middleware","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fmiddleware-examples","18.web-development\u002F09.nestjs-validation-pipeline\u002F09.middleware-examples",{"title":4516,"path":4517,"stem":4518},"Guards: захисні перехоплювачі доступу","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fguards-concept","18.web-development\u002F09.nestjs-validation-pipeline\u002F10.guards-concept",{"title":4520,"path":4521,"stem":4522},"Створення кастомних Guards","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fcustom-guards","18.web-development\u002F09.nestjs-validation-pipeline\u002F11.custom-guards",{"title":4524,"path":4525,"stem":4526},"Interceptors: перехоплення та трансформація відповідей","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Finterceptors-concept","18.web-development\u002F09.nestjs-validation-pipeline\u002F12.interceptors-concept",{"title":4528,"path":4529,"stem":4530},"Практичні приклади Interceptors","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Finterceptor-examples","18.web-development\u002F09.nestjs-validation-pipeline\u002F13.interceptor-examples",{"title":4532,"path":4533,"stem":4534},"Exception Filters: обробка виключень","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fexception-filters","18.web-development\u002F09.nestjs-validation-pipeline\u002F14.exception-filters",{"title":4536,"path":4537,"stem":4538},"Custom Exception Filters: спеціалізовані фільтри","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fcustom-exception-filters","18.web-development\u002F09.nestjs-validation-pipeline\u002F15.custom-exception-filters",{"title":4540,"path":4541,"stem":4542},"Execution Context: контекст виконання","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fexecution-context","18.web-development\u002F09.nestjs-validation-pipeline\u002F16.execution-context",{"title":4544,"path":4545,"stem":4546},"Custom Decorators: власні декоратори","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fcustom-decorators","18.web-development\u002F09.nestjs-validation-pipeline\u002F17.custom-decorators",{"title":4548,"path":4549,"stem":4550},"Global Components: глобальні компоненти","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fglobal-components","18.web-development\u002F09.nestjs-validation-pipeline\u002F18.global-components",{"title":4552,"path":4553,"stem":4554},"Pipeline Best Practices: найкращі практики","\u002Fweb-development\u002Fnestjs-validation-pipeline\u002Fpipeline-best-practices","18.web-development\u002F09.nestjs-validation-pipeline\u002F19.pipeline-best-practices",{"title":4556,"icon":2865,"path":4557,"stem":4558,"children":4559,"page":59},"Інтеграція реляційних баз даних засобами TypeORM","\u002Fweb-development\u002Ftypeorm-integration","18.web-development\u002F10.typeorm-integration",[4560,4564,4568,4572,4576,4580,4584],{"title":4561,"path":4562,"stem":4563},"Робота з базами даних у Node.js через нативні драйвери","\u002Fweb-development\u002Ftypeorm-integration\u002F01-nodejs-database-drivers","18.web-development\u002F10.typeorm-integration\u002F01-nodejs-database-drivers",{"title":4565,"path":4566,"stem":4567},"ORM та знайомство з TypeORM","\u002Fweb-development\u002Ftypeorm-integration\u002F02-orm-and-typeorm-intro","18.web-development\u002F10.typeorm-integration\u002F02-orm-and-typeorm-intro",{"title":4569,"path":4570,"stem":4571},"Налаштування TypeORM у NestJS проєкті","\u002Fweb-development\u002Ftypeorm-integration\u002F03-typeorm-setup-nestjs","18.web-development\u002F10.typeorm-integration\u002F03-typeorm-setup-nestjs",{"title":4573,"path":4574,"stem":4575},"Entity класи та декоратори TypeORM","\u002Fweb-development\u002Ftypeorm-integration\u002F04-entities-and-decorators","18.web-development\u002F10.typeorm-integration\u002F04-entities-and-decorators",{"title":4577,"path":4578,"stem":4579},"Repository Pattern та CRUD операції","\u002Fweb-development\u002Ftypeorm-integration\u002F05-repository-pattern-crud","18.web-development\u002F10.typeorm-integration\u002F05-repository-pattern-crud",{"title":4581,"path":4582,"stem":4583},"Пагінація та фільтрація даних","\u002Fweb-development\u002Ftypeorm-integration\u002F06-pagination-filtering","18.web-development\u002F10.typeorm-integration\u002F06-pagination-filtering",{"title":4585,"path":4586,"stem":4587},"QueryBuilder для складних запитів","\u002Fweb-development\u002Ftypeorm-integration\u002F07-query-builder","18.web-development\u002F10.typeorm-integration\u002F07-query-builder",{"title":4589,"icon":4590,"path":4591,"stem":4592,"children":4593,"page":59},"Зв'язки між сутностями, міграції та оптимізація запитів","i-lucide-git-branch","\u002Fweb-development\u002Fentity-relations-migrations","18.web-development\u002F11.entity-relations-migrations",[4594,4598,4602,4606,4610],{"title":4595,"path":4596,"stem":4597},"Зв'язки One-to-Many та Many-to-One","\u002Fweb-development\u002Fentity-relations-migrations\u002F01-one-to-many-many-to-one","18.web-development\u002F11.entity-relations-migrations\u002F01-one-to-many-many-to-one",{"title":4599,"path":4600,"stem":4601},"Зв'язки One-to-One та Many-to-Many","\u002Fweb-development\u002Fentity-relations-migrations\u002F02-one-to-one-many-to-many","18.web-development\u002F11.entity-relations-migrations\u002F02-one-to-one-many-to-many",{"title":4603,"path":4604,"stem":4605},"Керування міграціями бази даних","\u002Fweb-development\u002Fentity-relations-migrations\u002F03-migrations-management","18.web-development\u002F11.entity-relations-migrations\u002F03-migrations-management",{"title":4607,"path":4608,"stem":4609},"Транзакції у TypeORM","\u002Fweb-development\u002Fentity-relations-migrations\u002F04-transactions","18.web-development\u002F11.entity-relations-migrations\u002F04-transactions",{"title":4611,"path":4612,"stem":4613,"children":4614},"Індекси та оптимізація запитів","\u002Fweb-development\u002Fentity-relations-migrations\u002F05-indexes-optimization","18.web-development\u002F11.entity-relations-migrations\u002F05-indexes-optimization",[4615],{"title":4611,"path":4612,"stem":4613},{"title":4617,"icon":4618,"path":4619,"stem":4620,"children":4621,"page":59},"Механізми автентифікації у вебзастосунках","i-lucide-key","\u002Fweb-development\u002Fauthentication","18.web-development\u002F12.authentication",[4622,4626,4630,4634,4638,4642,4646,4650,4654],{"title":4623,"path":4624,"stem":4625},"Автентифікація vs Авторизація","\u002Fweb-development\u002Fauthentication\u002F01-authentication-vs-authorization","18.web-development\u002F12.authentication\u002F01-authentication-vs-authorization",{"title":4627,"path":4628,"stem":4629},"Стратегії автентифікації","\u002Fweb-development\u002Fauthentication\u002F02-authentication-strategies","18.web-development\u002F12.authentication\u002F02-authentication-strategies",{"title":4631,"path":4632,"stem":4633},"Автентифікація на основі сесій","\u002Fweb-development\u002Fauthentication\u002F03-session-based-authentication","18.web-development\u002F12.authentication\u002F03-session-based-authentication",{"title":4635,"path":4636,"stem":4637},"Структура JSON Web Token (JWT)","\u002Fweb-development\u002Fauthentication\u002F04-jwt-structure","18.web-development\u002F12.authentication\u002F04-jwt-structure",{"title":4639,"path":4640,"stem":4641},"Інтеграція JWT у NestJS","\u002Fweb-development\u002Fauthentication\u002F05-nestjs-jwt-integration","18.web-development\u002F12.authentication\u002F05-nestjs-jwt-integration",{"title":4643,"path":4644,"stem":4645},"Passport.js стратегії у NestJS","\u002Fweb-development\u002Fauthentication\u002F06-passport-strategies","18.web-development\u002F12.authentication\u002F06-passport-strategies",{"title":4647,"path":4648,"stem":4649},"Безпечне зберігання паролів: хешування та криптографічний захист","\u002Fweb-development\u002Fauthentication\u002F07-password-hashing","18.web-development\u002F12.authentication\u002F07-password-hashing",{"title":4651,"path":4652,"stem":4653},"Refresh Token та безпечне оновлення доступу","\u002Fweb-development\u002Fauthentication\u002F08-refresh-tokens","18.web-development\u002F12.authentication\u002F08-refresh-tokens",{"title":4655,"path":4656,"stem":4657},"OAuth 2.0 та OpenID Connect: делегована автентифікація","\u002Fweb-development\u002Fauthentication\u002F09-oauth2-openid","18.web-development\u002F12.authentication\u002F09-oauth2-openid",{"title":4659,"icon":4660,"path":4661,"stem":4662,"children":4663,"page":59},"Авторизація та безпека серверних застосунків","i-lucide-shield","\u002Fweb-development\u002Fauthorization-security","18.web-development\u002F13.authorization-security",[4664,4668,4672,4676,4680,4684,4688,4692,4696,4700],{"title":4665,"path":4666,"stem":4667},"Моделі авторизації","\u002Fweb-development\u002Fauthorization-security\u002F01-authorization-models","18.web-development\u002F13.authorization-security\u002F01-authorization-models",{"title":4669,"path":4670,"stem":4671},"Реалізація RBAC у NestJS","\u002Fweb-development\u002Fauthorization-security\u002F02-rbac-implementation","18.web-development\u002F13.authorization-security\u002F02-rbac-implementation",{"title":4673,"path":4674,"stem":4675},"Permission-Based Access Control","\u002Fweb-development\u002Fauthorization-security\u002F03-permission-based-access","18.web-development\u002F13.authorization-security\u002F03-permission-based-access",{"title":4677,"path":4678,"stem":4679},"ABAC та бібліотека CASL","\u002Fweb-development\u002Fauthorization-security\u002F04-abac-casl","18.web-development\u002F13.authorization-security\u002F04-abac-casl",{"title":4681,"path":4682,"stem":4683},"Кастомні декоратори та метадані","\u002Fweb-development\u002Fauthorization-security\u002F05-custom-decorators-metadata","18.web-development\u002F13.authorization-security\u002F05-custom-decorators-metadata",{"title":4685,"path":4686,"stem":4687},"Конфігурація CORS","\u002Fweb-development\u002Fauthorization-security\u002F06-cors-configuration","18.web-development\u002F13.authorization-security\u002F06-cors-configuration",{"title":4689,"path":4690,"stem":4691},"Захист через HTTP Security Headers (Helmet)","\u002Fweb-development\u002Fauthorization-security\u002F07-helmet-security-headers","18.web-development\u002F13.authorization-security\u002F07-helmet-security-headers",{"title":4693,"path":4694,"stem":4695},"Rate Limiting та Throttling","\u002Fweb-development\u002Fauthorization-security\u002F08-rate-limiting-throttling","18.web-development\u002F13.authorization-security\u002F08-rate-limiting-throttling",{"title":4697,"path":4698,"stem":4699},"Захист від поширених атак","\u002Fweb-development\u002Fauthorization-security\u002F09-common-attacks-protection","18.web-development\u002F13.authorization-security\u002F09-common-attacks-protection",{"title":4701,"path":4702,"stem":4703},"HTTPS у продакшені","\u002Fweb-development\u002Fauthorization-security\u002F10-https-production","18.web-development\u002F13.authorization-security\u002F10-https-production",{"title":4705,"icon":871,"path":4706,"stem":4707,"children":4708,"page":59},"Комунікація у реальному часі, нотифікації та фонові задачі","\u002Fweb-development\u002Frealtime-notifications-jobs","18.web-development\u002F14.realtime-notifications-jobs",[4709,4713,4717,4721,4725,4729,4732,4735,4739,4743],{"title":4710,"path":4711,"stem":4712},"Методи комунікації у реальному часі","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F01-realtime-communication-methods","18.web-development\u002F14.realtime-notifications-jobs\u002F01-realtime-communication-methods",{"title":4714,"path":4715,"stem":4716},"WebSocket Gateway у NestJS","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F02-websocket-gateway","18.web-development\u002F14.realtime-notifications-jobs\u002F02-websocket-gateway",{"title":4718,"path":4719,"stem":4720},"Lifecycle та Rooms у WebSocket","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F03-websocket-lifecycle-rooms","18.web-development\u002F14.realtime-notifications-jobs\u002F03-websocket-lifecycle-rooms",{"title":4722,"path":4723,"stem":4724},"Server-Sent Events (SSE)","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F04-server-sent-events","18.web-development\u002F14.realtime-notifications-jobs\u002F04-server-sent-events",{"title":4726,"path":4727,"stem":4728},"Архітектура системи нотифікацій","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F05-notification-system","18.web-development\u002F14.realtime-notifications-jobs\u002F05-notification-system",{"title":904,"path":4730,"stem":4731},"\u002Fweb-development\u002Frealtime-notifications-jobs\u002F06-email-notifications","18.web-development\u002F14.realtime-notifications-jobs\u002F06-email-notifications",{"title":900,"path":4733,"stem":4734},"\u002Fweb-development\u002Frealtime-notifications-jobs\u002F07-web-push-notifications","18.web-development\u002F14.realtime-notifications-jobs\u002F07-web-push-notifications",{"title":4736,"path":4737,"stem":4738},"Фонові задачі та черги","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F08-background-jobs-queues","18.web-development\u002F14.realtime-notifications-jobs\u002F08-background-jobs-queues",{"title":4740,"path":4741,"stem":4742},"Планувальники та Cron jobs","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F09-schedulers-cron","18.web-development\u002F14.realtime-notifications-jobs\u002F09-schedulers-cron",{"title":4744,"path":4745,"stem":4746},"Практичні сценарії застосування","\u002Fweb-development\u002Frealtime-notifications-jobs\u002F10-practical-scenarios","18.web-development\u002F14.realtime-notifications-jobs\u002F10-practical-scenarios",{"title":4748,"icon":4749,"path":4750,"stem":4751,"children":4752,"page":59},"Тестування, документування та розгортання застосунків","i-lucide-rocket","\u002Fweb-development\u002Ftesting-documentation-deployment","18.web-development\u002F15.testing-documentation-deployment",[4753,4757,4761,4765,4769,4773,4777,4781,4785,4789],{"title":4754,"path":4755,"stem":4756},"Піраміда тестування та стратегія","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F01-testing-pyramid-strategy","18.web-development\u002F15.testing-documentation-deployment\u002F01-testing-pyramid-strategy",{"title":4758,"path":4759,"stem":4760},"Основи Jest у NestJS","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F02-jest-nestjs-basics","18.web-development\u002F15.testing-documentation-deployment\u002F02-jest-nestjs-basics",{"title":4762,"path":4763,"stem":4764},"Unit-тестування сервісів","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F03-unit-testing-services","18.web-development\u002F15.testing-documentation-deployment\u002F03-unit-testing-services",{"title":4766,"path":4767,"stem":4768},"E2E тестування та покриття коду","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F04-e2e-testing-coverage","18.web-development\u002F15.testing-documentation-deployment\u002F04-e2e-testing-coverage",{"title":4770,"path":4771,"stem":4772},"Swagger, OpenAPI, Scalar та Arazzo","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F05-swagger-openapi","18.web-development\u002F15.testing-documentation-deployment\u002F05-swagger-openapi",{"title":4774,"path":4775,"stem":4776},"Керування конфігурацією","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F06-configuration-management","18.web-development\u002F15.testing-documentation-deployment\u002F06-configuration-management",{"title":4778,"path":4779,"stem":4780},"Docker та контейнеризація","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F07-docker-containerization","18.web-development\u002F15.testing-documentation-deployment\u002F07-docker-containerization",{"title":4782,"path":4783,"stem":4784},"CI\u002FCD Pipelines","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F08-cicd-pipelines","18.web-development\u002F15.testing-documentation-deployment\u002F08-cicd-pipelines",{"title":4786,"path":4787,"stem":4788},"Логування та моніторинг","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F09-logging-monitoring","18.web-development\u002F15.testing-documentation-deployment\u002F09-logging-monitoring",{"title":4790,"path":4791,"stem":4792},"Серіалізація та компресія","\u002Fweb-development\u002Ftesting-documentation-deployment\u002F10-serialization-compression","18.web-development\u002F15.testing-documentation-deployment\u002F10-serialization-compression",{"title":4794,"icon":4795,"path":4796,"stem":4797,"children":4798,"page":59},"AI Core: мислення AI","i-lucide-brain","\u002Fai-core","19.ai-core",[4799,4840,4889,4927],{"title":4800,"icon":4749,"path":4801,"stem":4802,"children":4803,"page":59},"Вхід у професію AI Native Developer","\u002Fai-core\u002Fai-native-developer","19.ai-core\u002F01.ai-native-developer",[4804,4808,4812,4816,4820,4824,4828,4832,4836],{"title":4805,"path":4806,"stem":4807},"Вступ до професії розробника цифрових продуктів","\u002Fai-core\u002Fai-native-developer\u002Fdigital-products","19.ai-core\u002F01.ai-native-developer\u002F01.digital-products",{"title":4809,"path":4810,"stem":4811},"Професія розробника програмного забезпечення","\u002Fai-core\u002Fai-native-developer\u002Fsoftware-developer","19.ai-core\u002F01.ai-native-developer\u002F02.software-developer",{"title":4813,"path":4814,"stem":4815},"Професія AI Native Developer","\u002Fai-core\u002Fai-native-developer\u002Fai-native-developer","19.ai-core\u002F01.ai-native-developer\u002F03.ai-native-developer",{"title":4817,"path":4818,"stem":4819},"Вплив AI на розробку програмного забезпечення","\u002Fai-core\u002Fai-native-developer\u002Fai-impact-on-development","19.ai-core\u002F01.ai-native-developer\u002F04.ai-impact-on-development",{"title":4821,"path":4822,"stem":4823},"Життєвий цикл цифрового продукту","\u002Fai-core\u002Fai-native-developer\u002Fproduct-lifecycle","19.ai-core\u002F01.ai-native-developer\u002F05.product-lifecycle",{"title":4825,"path":4826,"stem":4827},"Застосування AI на етапах створення цифрового продукту","\u002Fai-core\u002Fai-native-developer\u002Fai-in-product-creation","19.ai-core\u002F01.ai-native-developer\u002F06.ai-in-product-creation",{"title":4829,"path":4830,"stem":4831},"Розподіл завдань між людиною та AI","\u002Fai-core\u002Fai-native-developer\u002Fhuman-vs-ai-tasks","19.ai-core\u002F01.ai-native-developer\u002F07.human-vs-ai-tasks",{"title":4833,"path":4834,"stem":4835},"Компетенції AI Native Developer","\u002Fai-core\u002Fai-native-developer\u002Fcompetencies","19.ai-core\u002F01.ai-native-developer\u002F08.competencies",{"title":4837,"path":4838,"stem":4839},"Карта професійного розвитку AI Native Developer","\u002Fai-core\u002Fai-native-developer\u002Fcareer-roadmap","19.ai-core\u002F01.ai-native-developer\u002F09.career-roadmap",{"title":4841,"icon":255,"path":4842,"stem":4843,"children":4844,"page":59},"Штучний інтелект і машинне навчання","\u002Fai-core\u002Fai-and-machine-learning","19.ai-core\u002F02.ai-and-machine-learning",[4845,4849,4853,4857,4861,4865,4869,4873,4877,4881,4885],{"title":4846,"path":4847,"stem":4848},"Штучний інтелект","\u002Fai-core\u002Fai-and-machine-learning\u002Fwhat-is-ai","19.ai-core\u002F02.ai-and-machine-learning\u002F01.what-is-ai",{"title":4850,"path":4851,"stem":4852},"Відмінність AI від інших програмних систем","\u002Fai-core\u002Fai-and-machine-learning\u002Fai-vs-automation","19.ai-core\u002F02.ai-and-machine-learning\u002F02.ai-vs-automation",{"title":4854,"path":4855,"stem":4856},"Завдання штучного інтелекту","\u002Fai-core\u002Fai-and-machine-learning\u002Fai-tasks","19.ai-core\u002F02.ai-and-machine-learning\u002F03.ai-tasks",{"title":4858,"path":4859,"stem":4860},"Machine Learning","\u002Fai-core\u002Fai-and-machine-learning\u002Fmachine-learning","19.ai-core\u002F02.ai-and-machine-learning\u002F04.machine-learning",{"title":4862,"path":4863,"stem":4864},"Machine Learning Workflow","\u002Fai-core\u002Fai-and-machine-learning\u002Fml-workflow","19.ai-core\u002F02.ai-and-machine-learning\u002F05.ml-workflow",{"title":4866,"path":4867,"stem":4868},"Типи машинного навчання","\u002Fai-core\u002Fai-and-machine-learning\u002Fml-types","19.ai-core\u002F02.ai-and-machine-learning\u002F06.ml-types",{"title":4870,"path":4871,"stem":4872},"Generative AI","\u002Fai-core\u002Fai-and-machine-learning\u002Fgenerative-ai","19.ai-core\u002F02.ai-and-machine-learning\u002F07.generative-ai",{"title":4874,"path":4875,"stem":4876},"Типи контенту, створюваного AI","\u002Fai-core\u002Fai-and-machine-learning\u002Fai-content-types","19.ai-core\u002F02.ai-and-machine-learning\u002F08.ai-content-types",{"title":4878,"path":4879,"stem":4880},"Великі мовні моделі","\u002Fai-core\u002Fai-and-machine-learning\u002Flarge-language-models","19.ai-core\u002F02.ai-and-machine-learning\u002F09.large-language-models",{"title":4882,"path":4883,"stem":4884},"Можливості та обмеження Generative AI","\u002Fai-core\u002Fai-and-machine-learning\u002Fcapabilities-and-limitations","19.ai-core\u002F02.ai-and-machine-learning\u002F10.capabilities-and-limitations",{"title":4886,"path":4887,"stem":4888},"Сучасні AI-асистенти","\u002Fai-core\u002Fai-and-machine-learning\u002Fmodern-ai-assistants","19.ai-core\u002F02.ai-and-machine-learning\u002F11.modern-ai-assistants",{"title":4890,"icon":4891,"path":4892,"stem":4893,"children":4894,"page":59},"Постановка завдань для AI: принципи створення промптів","i-lucide-message-square-text","\u002Fai-core\u002Fprompt-engineering","19.ai-core\u002F03.prompt-engineering",[4895,4899,4903,4907,4911,4915,4919,4923],{"title":4896,"path":4897,"stem":4898},"Промпт як інструмент","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-as-tool","19.ai-core\u002F03.prompt-engineering\u002F01.prompt-as-tool",{"title":4900,"path":4901,"stem":4902},"Принципи створення промптів","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-principles","19.ai-core\u002F03.prompt-engineering\u002F02.prompt-principles",{"title":4904,"path":4905,"stem":4906},"Структура промпта","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-structure","19.ai-core\u002F03.prompt-engineering\u002F03.prompt-structure",{"title":4908,"path":4909,"stem":4910},"Види промптів за типом задачі","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-types","19.ai-core\u002F03.prompt-engineering\u002F04.prompt-types",{"title":4912,"path":4913,"stem":4914},"Прийоми посилення промпта","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-enhancement","19.ai-core\u002F03.prompt-engineering\u002F05.prompt-enhancement",{"title":4916,"path":4917,"stem":4918},"Розбір і покращення промпта","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-analysis","19.ai-core\u002F03.prompt-engineering\u002F06.prompt-analysis",{"title":4920,"path":4921,"stem":4922},"Типові помилки під час створення промптів","\u002Fai-core\u002Fprompt-engineering\u002Fcommon-mistakes","19.ai-core\u002F03.prompt-engineering\u002F07.common-mistakes",{"title":4924,"path":4925,"stem":4926},"Практикум: особиста бібліотека промптів","\u002Fai-core\u002Fprompt-engineering\u002Fprompt-library","19.ai-core\u002F03.prompt-engineering\u002F08.prompt-library",{"title":4928,"icon":793,"path":4929,"stem":4930,"children":4931,"page":59},"Перевірка результатів, безпека та AI Native Workflow","\u002Fai-core\u002Fverification-security-workflow","19.ai-core\u002F04.verification-security-workflow",[4932,4936,4940,4944,4948,4952,4956,4960,4964],{"title":4933,"path":4934,"stem":4935},"Межі можливостей AI","\u002Fai-core\u002Fverification-security-workflow\u002Fai-limitations","19.ai-core\u002F04.verification-security-workflow\u002F01.ai-limitations",{"title":4937,"path":4938,"stem":4939},"Галюцинації AI","\u002Fai-core\u002Fverification-security-workflow\u002Fai-hallucinations","19.ai-core\u002F04.verification-security-workflow\u002F02.ai-hallucinations",{"title":4941,"path":4942,"stem":4943},"Критичне мислення під час роботи з AI","\u002Fai-core\u002Fverification-security-workflow\u002Fcritical-thinking","19.ai-core\u002F04.verification-security-workflow\u002F03.critical-thinking",{"title":4945,"path":4946,"stem":4947},"Перевірка результатів роботи AI","\u002Fai-core\u002Fverification-security-workflow\u002Fresult-verification","19.ai-core\u002F04.verification-security-workflow\u002F04.result-verification",{"title":4949,"path":4950,"stem":4951},"Захист даних під час роботи з AI","\u002Fai-core\u002Fverification-security-workflow\u002Fdata-protection","19.ai-core\u002F04.verification-security-workflow\u002F05.data-protection",{"title":4953,"path":4954,"stem":4955},"Авторське право та навчальна доброчесність","\u002Fai-core\u002Fverification-security-workflow\u002Fcopyright-integrity","19.ai-core\u002F04.verification-security-workflow\u002F06.copyright-integrity",{"title":4957,"path":4958,"stem":4959},"Відповідальне використання AI","\u002Fai-core\u002Fverification-security-workflow\u002Fresponsible-use","19.ai-core\u002F04.verification-security-workflow\u002F07.responsible-use",{"title":4961,"path":4962,"stem":4963},"AI Native Workflow","\u002Fai-core\u002Fverification-security-workflow\u002Fai-native-workflow","19.ai-core\u002F04.verification-security-workflow\u002F08.ai-native-workflow",{"title":4965,"path":4966,"stem":4967},"Застосування AI Native Workflow під час створення цифрового продукту","\u002Fai-core\u002Fverification-security-workflow\u002Fworkflow-in-practice","19.ai-core\u002F04.verification-security-workflow\u002F09.workflow-in-practice",{"title":4969,"icon":4970,"path":4971,"stem":4972,"children":4973,"page":59},"AI Developer: Vibe Coding Sprint","i-lucide-code-xml","\u002Fai-developer","20.ai-developer",[4974,5000,5022,5043],{"title":4975,"icon":4976,"path":4977,"stem":4978,"children":4979,"page":59},"Проєктування і чорнова версія","i-lucide-pencil-ruler","\u002Fai-developer\u002Fdraft-creation","20.ai-developer\u002F01.draft-creation",[4980,4984,4988,4992,4996],{"title":4981,"path":4982,"stem":4983},"Парадигма Vibe Coding Workflow","\u002Fai-developer\u002Fdraft-creation\u002Fvibe-coding-workflow","20.ai-developer\u002F01.draft-creation\u002F01.vibe-coding-workflow",{"title":4985,"path":4986,"stem":4987},"AI-середовище швидкої розробки","\u002Fai-developer\u002Fdraft-creation\u002Fai-ide-environment","20.ai-developer\u002F01.draft-creation\u002F02.ai-ide-environment",{"title":4989,"path":4990,"stem":4991},"Користувацьке завдання та межі першої версії","\u002Fai-developer\u002Fdraft-creation\u002Fproduct-scope-and-mvp","20.ai-developer\u002F01.draft-creation\u002F03.product-scope-and-mvp",{"title":4993,"path":4994,"stem":4995},"Архітектура інтерфейсу та стани застосунку","\u002Fai-developer\u002Fdraft-creation\u002Fapplication-architecture-and-states","20.ai-developer\u002F01.draft-creation\u002F04.application-architecture-and-states",{"title":4997,"path":4998,"stem":4999},"Генерація та фіксація чорнової версії","\u002Fai-developer\u002Fdraft-creation\u002Fgenerating-draft-version","20.ai-developer\u002F01.draft-creation\u002F05.generating-draft-version",{"title":5001,"icon":5002,"path":5003,"stem":5004,"children":5005,"page":59},"Перша робоча версія","i-lucide-play-circle","\u002Fai-developer\u002Ffirst-working-version","20.ai-developer\u002F02.first-working-version",[5006,5010,5014,5018],{"title":5007,"path":5008,"stem":5009},"Уточнення контексту проєкту для AI","\u002Fai-developer\u002Ffirst-working-version\u002Frefining-project-context","20.ai-developer\u002F02.first-working-version\u002F01.refining-project-context",{"title":5011,"path":5012,"stem":5013},"Стратегія завдань для AI-розробки","\u002Fai-developer\u002Ffirst-working-version\u002Fai-prompting-strategy","20.ai-developer\u002F02.first-working-version\u002F02.ai-prompting-strategy",{"title":5015,"path":5016,"stem":5017},"Доопрацювання до першої робочої версії","\u002Fai-developer\u002Ffirst-working-version\u002Frefining-to-working-version","20.ai-developer\u002F02.first-working-version\u002F03.refining-to-working-version",{"title":5019,"path":5020,"stem":5021},"Перевірка першої робочої версії","\u002Fai-developer\u002Ffirst-working-version\u002Ftesting-first-working-version","20.ai-developer\u002F02.first-working-version\u002F04.testing-first-working-version",{"title":5023,"icon":3072,"path":5024,"stem":5025,"children":5026,"page":59},"Ітерації, дебагінг та відновлення","\u002Fai-developer\u002Fiteration-debugging-recovery","20.ai-developer\u002F03.iteration-debugging-recovery",[5027,5031,5035,5039],{"title":5028,"path":5029,"stem":5030},"Управління точковими змінами продукту","\u002Fai-developer\u002Fiteration-debugging-recovery\u002Fchange-management","20.ai-developer\u002F03.iteration-debugging-recovery\u002F01.change-management",{"title":5032,"path":5033,"stem":5034},"Надійність інтерфейсу та обробка помилок","\u002Fai-developer\u002Fiteration-debugging-recovery\u002Fscenario-reliability-and-errors","20.ai-developer\u002F03.iteration-debugging-recovery\u002F02.scenario-reliability-and-errors",{"title":5036,"path":5037,"stem":5038},"Керування контекстом і збереження версій","\u002Fai-developer\u002Fiteration-debugging-recovery\u002Fproject-context-and-versions","20.ai-developer\u002F03.iteration-debugging-recovery\u002F03.project-context-and-versions",{"title":5040,"path":5041,"stem":5042},"Збої AI-розробки, ліміти та аварійне відновлення","\u002Fai-developer\u002Fiteration-debugging-recovery\u002Fai-breakdowns-and-recovery","20.ai-developer\u002F03.iteration-debugging-recovery\u002F04.ai-breakdowns-and-recovery",{"title":5044,"icon":5045,"path":5046,"stem":5047,"children":5048,"page":59},"Перевірка та завершення продукту","i-lucide-check-check","\u002Fai-developer\u002Fverification-and-completion","20.ai-developer\u002F04.verification-and-completion",[5049,5053,5057],{"title":5050,"path":5051,"stem":5052},"Комплексний аудит та підсумкове тестування","\u002Fai-developer\u002Fverification-and-completion\u002Ffinal-product-testing","20.ai-developer\u002F04.verification-and-completion\u002F01.final-product-testing",{"title":5054,"path":5055,"stem":5056},"Підготовка підсумкової версії коду","\u002Fai-developer\u002Fverification-and-completion\u002Fpreparing-release-version","20.ai-developer\u002F04.verification-and-completion\u002F02.preparing-release-version",{"title":5058,"path":5059,"stem":5060},"Демонстрація, публікація та критерії оцінювання","\u002Fai-developer\u002Fverification-and-completion\u002Fproduct-showcase-and-publishing","20.ai-developer\u002F04.verification-and-completion\u002F03.product-showcase-and-publishing",{"title":5062,"path":5063,"stem":5064,"children":5065,"page":59},"Tailwind","\u002Ftailwind","21.tailwind",[5066,5070,5074,5078,5082,5086,5090,5094,5098,5102,5106,5110],{"title":5067,"path":5068,"stem":5069},"Що таке Tailwind CSS і навіщо він потрібен","\u002Ftailwind\u002Ftailwind-intro-philosophy","21.tailwind\u002F01.tailwind-intro-philosophy",{"title":5071,"path":5072,"stem":5073},"Встановлення та налаштування Tailwind CSS v4","\u002Ftailwind\u002Ftailwind-installation-setup","21.tailwind\u002F02.tailwind-installation-setup",{"title":5075,"path":5076,"stem":5077},"Utility-класи: основи та система Tailwind","\u002Ftailwind\u002Ftailwind-utility-classes-core","21.tailwind\u002F03.tailwind-utility-classes-core",{"title":5079,"path":5080,"stem":5081},"Layout: Flexbox та Grid через Tailwind","\u002Ftailwind\u002Ftailwind-flexbox-grid","21.tailwind\u002F04.tailwind-flexbox-grid",{"title":5083,"path":5084,"stem":5085},"Кастомізація теми через @theme у Tailwind v4","\u002Ftailwind\u002Ftailwind-theme-customization","21.tailwind\u002F05.tailwind-theme-customization",{"title":5087,"path":5088,"stem":5089},"Варіанти: hover, focus, responsive, dark mode та нові v4","\u002Ftailwind\u002Ftailwind-variants-states","21.tailwind\u002F06.tailwind-variants-states",{"title":5091,"path":5092,"stem":5093},"Типографіка та система кольорів у Tailwind v4","\u002Ftailwind\u002Ftailwind-typography-colors","21.tailwind\u002F07.tailwind-typography-colors",{"title":5095,"path":5096,"stem":5097},"Компоненти та повторюваність: @apply, @utility та патерни","\u002Ftailwind\u002Ftailwind-components-patterns","21.tailwind\u002F08.tailwind-components-patterns",{"title":5099,"path":5100,"stem":5101},"Темна тема та система дизайн-токенів у Tailwind v4","\u002Ftailwind\u002Ftailwind-dark-mode-theming","21.tailwind\u002F09.tailwind-dark-mode-theming",{"title":5103,"path":5104,"stem":5105},"Довільні значення та контейнерні запити у Tailwind v4","\u002Ftailwind\u002Ftailwind-arbitrary-container-queries","21.tailwind\u002F10.tailwind-arbitrary-container-queries",{"title":5107,"path":5108,"stem":5109},"Анімації, трансформації та 3D у Tailwind v4","\u002Ftailwind\u002Ftailwind-animations-transforms","21.tailwind\u002F11.tailwind-animations-transforms",{"title":5111,"path":5112,"stem":5113},"Tailwind CLI, PostCSS та інтеграція з фреймворками","\u002Ftailwind\u002Ftailwind-cli-tooling","21.tailwind\u002F12.tailwind-cli-tooling",{"title":5115,"path":5116,"stem":5117,"children":5118,"page":59},"React Typescript","\u002Freact-typescript","22.react-typescript",[5119,5144,5148,5152,5156,5160,5164,5168,5172],{"title":5120,"path":5121,"stem":5122,"children":5123,"page":59},"From Html To Project","\u002Freact-typescript\u002Ffrom-html-to-project","22.react-typescript\u002F01.from-html-to-project",[5124,5128,5132,5136,5140],{"title":5125,"path":5126,"stem":5127},"Від одного HTML-файлу до повноцінного проєкту","\u002Freact-typescript\u002Ffrom-html-to-project\u002Fevolution","22.react-typescript\u002F01.from-html-to-project\u002F01.evolution",{"title":5129,"path":5130,"stem":5131},"Що таке React","\u002Freact-typescript\u002Ffrom-html-to-project\u002Fwhat-is-react","22.react-typescript\u002F01.from-html-to-project\u002F02.what-is-react",{"title":5133,"path":5134,"stem":5135},"JSX та TSX — синтаксичний цукор, а не магія","\u002Freact-typescript\u002Ffrom-html-to-project\u002Fjsx-tsx-syntax","22.react-typescript\u002F01.from-html-to-project\u002F03.jsx-tsx-syntax",{"title":5137,"path":5138,"stem":5139},"Компоненти — будівельні блоки інтерфейсу","\u002Freact-typescript\u002Ffrom-html-to-project\u002Fcomponents","22.react-typescript\u002F01.from-html-to-project\u002F04.components",{"title":5141,"path":5142,"stem":5143},"JSX — розмітка всередині TypeScript","\u002Freact-typescript\u002Ffrom-html-to-project\u002Fjsx-expressions","22.react-typescript\u002F01.from-html-to-project\u002F05.jsx-expressions",{"title":5145,"path":5146,"stem":5147},"Хук useReducer: структуроване управління складним станом","\u002Freact-typescript\u002Fuse-reducer","22.react-typescript\u002F23.use-reducer",{"title":5149,"path":5150,"stem":5151},"Context API: передавання даних без «пропс-дрілінгу»","\u002Freact-typescript\u002Fcontext-api","22.react-typescript\u002F24.context-api",{"title":5153,"path":5154,"stem":5155},"Синхронізація з ефектами: хук useEffect та функції очищення","\u002Freact-typescript\u002Fuse-effect","22.react-typescript\u002F27.use-effect",{"title":5157,"path":5158,"stem":5159},"Можливо, вам не потрібен ефект: антипатерни та альтернативи","\u002Freact-typescript\u002Fyou-might-not-need-effect","22.react-typescript\u002F28.you-might-not-need-effect",{"title":5161,"path":5162,"stem":5163},"Життєвий цикл реактивних ефектів: мислення процесами синхронізації","\u002Freact-typescript\u002Flifecycle-of-reactive-effects","22.react-typescript\u002F29.lifecycle-of-reactive-effects",{"title":5165,"path":5166,"stem":5167},"Розділення подій та ефектів: реактивна проти нереактивної логіки","\u002Freact-typescript\u002Fseparating-events-from-effects","22.react-typescript\u002F30.separating-events-from-effects",{"title":5169,"path":5170,"stem":5171},"Видалення зайвих залежностей ефектів та оптимізація синхронізації","\u002Freact-typescript\u002Fremoving-effect-dependencies","22.react-typescript\u002F31.removing-effect-dependencies",{"title":5173,"path":5174,"stem":5175},"Кастомні хуки: повторне використання та композиція реактивної логіки","\u002Freact-typescript\u002Fcustom-hooks","22.react-typescript\u002F32.custom-hooks",{"title":5177,"path":5178,"stem":5179},"Тестування компонентів діаграм та пісочниць","\u002Ftest-components","98.test-components",{"id":5181,"title":3901,"body":5182,"description":20816,"extension":20817,"links":20818,"meta":20819,"navigation":5516,"path":3902,"seo":20820,"stem":3903,"__hash__":20821},"docs\u002F16.ai-python\u002F13.pytorch-intro\u002F01.tensors-and-autograd.md",{"type":5183,"value":5184,"toc":20793},"minimark",[5185,5256,5261,5265,5274,5293,5350,5359,5363,5366,5478,5486,5489,5687,5690,5694,5699,5706,5713,5720,5761,5971,5975,5978,5983,6587,6603,6607,6610,7009,7032,7036,7039,7497,7501,7504,7777,7790,8002,8006,8023,8586,8593,8609,8646,8650,8653,8940,8952,8959,9014,9025,9028,9431,9478,9488,9491,9825,9880,9884,9897,10209,10236,10240,10243,10247,10250,10656,10660,10663,11255,11262,11264,11268,11273,11280,11316,11736,11743,11749,11768,12071,12121,12125,12128,12133,12696,12700,12703,13042,13072,13079,13092,13096,13099,13763,13779,13783,13786,13790,13799,14191,14205,14209,14216,14665,14685,14689,14696,14970,14977,14979,14983,14986,15333,15356,15358,15362,15365,15369,15372,15394,15399,15402,15405,15411,15414,15417,15420,15743,15753,15773,15780,15784,15796,16019,16025,16032,16177,16191,16197,16200,16637,16655,16659,16662,16682,16963,16973,16980,16983,17209,17212,17221,17225,17232,17567,17584,17588,17601,17937,17950,17952,17956,17959,17966,17969,18255,18287,18294,18303,18564,18640,18647,18650,18906,18910,18913,18917,18923,19258,19262,19268,19745,19757,19764,19772,19776,19779,20377,20380,20382,20386,20512,20736,20738,20742,20749,20767,20789],[5186,5187,5188,5212],"card-group",{},[5189,5190,5193],"card",{"icon":5191,"title":5192},"i-lucide-target","🎯 Мета статті",[5194,5195,5196,5200,5203,5206,5209],"ul",{},[5197,5198,5199],"li",{},"Зрозуміти, що таке тензори та чим вони відрізняються від NumPy масивів",[5197,5201,5202],{},"Навчитися створювати та маніпулювати тензорами в PyTorch",[5197,5204,5205],{},"Опанувати операції broadcasting для ефективних обчислень",[5197,5207,5208],{},"Розібратися у механізмі автоматичного диференціювання (autograd)",[5197,5210,5211],{},"Навчитися обчислювати градієнти для складних функцій автоматично",[5189,5213,5215],{"icon":4618,"title":5214},"🔑 Ключові концепції",[5194,5216,5217,5229,5235,5244,5250],{},[5197,5218,5219,5228],{},[5220,5221,5222,5223,5227],"strong",{},"Тензор (",[5224,5225,5226],"em",{},"tensor",")"," — багатовимірний масив даних, основна структура PyTorch",[5197,5230,5231,5234],{},[5220,5232,5233],{},"Autograd"," — система автоматичного обчислення градієнтів",[5197,5236,5237,5243],{},[5220,5238,5239,5240,5227],{},"Граф обчислень (",[5224,5241,5242],{},"computational graph"," — структура для відстеження операцій",[5197,5245,5246,5249],{},[5220,5247,5248],{},"Broadcasting"," — автоматичне розширення форм для сумісності операцій",[5197,5251,5252,5255],{},[5220,5253,5254],{},"Backpropagation"," — алгоритм зворотного поширення помилки",[5257,5258,5260],"h2",{"id":5259},"вступ-чому-numpy-недостатньо-для-deep-learning","Вступ: Чому NumPy недостатньо для Deep Learning",[5262,5263,5264],"p",{},"У попередніх модулях ми активно використовували NumPy для роботи з даними, побудови лінійних моделей та базових нейронних мереж. NumPy — чудова бібліотека для наукових обчислень, але коли справа доходить до створення глибоких нейронних мереж, вона має три критичні обмеження:",[5262,5266,5267],{},[5268,5269],"img",{"alt":5270,"className":5271,"src":5273},"Архітектурне порівняння CPU та GPU для тензорних обчислень у PyTorch",[5272],"diagram-img","\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F01.png",[5275,5276,5278,5281,5284,5287,5290],"plant-uml",{"alt":5277},"Архітектурне порівняння CPU та GPU для тензорних обчислень",[5262,5279,5280],{},"@startuml\nskinparam backgroundColor #FFFFFF\nskinparam defaultFontName -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, sans-serif\nskinparam roundcorner 8\nskinparam shadowing false\nskinparam handwritten false",[5262,5282,5283],{},"skinparam rectangle {\nRoundCorner 8\nBorderColor #94A3B8\nFontSize 12\n}",[5262,5285,5286],{},"rectangle \"Центральний процесор (CPU)\" as cpu #EFF6FF {\ncard \"Мала кількість потужних ядер (4-16 ядер)\\nОптимізовано для послідовних складних інструкцій\" as cpu_cores\ncard \"Велика кеш-пам'ять (L1\u002FL2\u002FL3) + Низька затримка\" as cpu_cache\ncard \"Швидкість: базова для послідовних задач\" as cpu_speed\ncpu_cores -down-> cpu_cache\ncpu_cache -down-> cpu_speed\n}",[5262,5288,5289],{},"rectangle \"Графічний процесор (GPU)\" as gpu #ECFDF5 {\ncard \"Тисячі паралельних мікроядер (CUDA Cores, Tensor Cores)\\nОптимізовано для масових паралельних операцій SIMD\" as gpu_cores\ncard \"Висока пропускна здатність пам'яті (HBM \u002F GDDR6)\" as gpu_mem\ncard \"Швидкість: у 20-100 разів швидше для матричних множень\" as gpu_speed\ngpu_cores -down-> gpu_mem\ngpu_mem -down-> gpu_speed\n}",[5262,5291,5292],{},"@enduml",[5186,5294,5295,5317],{},[5189,5296,5299,5305,5311],{"icon":5297,"title":5298},"i-lucide-alert-triangle","🚫 Обмеження NumPy",[5262,5300,5301,5304],{},[5220,5302,5303],{},"1. Відсутність підтримки GPU","\nNumPy виконує всі обчислення на центральному процесорі (CPU). Для великих нейронних мереж це означає години або навіть дні тренування.",[5262,5306,5307,5310],{},[5220,5308,5309],{},"2. Немає автоматичного диференціювання","\nЩоб навчити нейронну мережу, потрібно обчислити градієнти для тисяч параметрів. У NumPy це доводиться робити вручну — десятки рядків коду для кожного шару.",[5262,5312,5313,5316],{},[5220,5314,5315],{},"3. Відсутність високорівневих компонентів","\nНемає готових реалізацій згорткових шарів, рекурентних мереж, attention-механізмів. Доводиться писати все з нуля.",[5189,5318,5321,5324],{"icon":5319,"title":5320},"i-lucide-check-circle","✅ Рішення: PyTorch",[5262,5322,5323],{},"PyTorch вирішує всі ці проблеми:",[5194,5325,5326,5332,5338,5344],{},[5197,5327,5328,5331],{},[5220,5329,5330],{},"GPU-прискорення:"," тренування у 20-100 разів швидше",[5197,5333,5334,5337],{},[5220,5335,5336],{},"Autograd:"," градієнти обчислюються автоматично для будь-якої функції",[5197,5339,5340,5343],{},[5220,5341,5342],{},"Neural Networks API:"," готові шари, функції втрат та оптимізатори",[5197,5345,5346,5349],{},[5220,5347,5348],{},"Динамічні графи:"," гнучкість як у Python, швидкість як у C++",[5351,5352,5353],"blockquote",{},[5262,5354,5355,5358],{},[5220,5356,5357],{},"Аналогія:"," Якщо NumPy — це як збирати меблі IKEA без інструкції та інструментів, то PyTorch — це повний набір професійних інструментів із покроковою інструкцією та підказками на кожному етапі.",[5257,5360,5362],{"id":5361},"встановлення-pytorch","Встановлення PyTorch",[5262,5364,5365],{},"Перед початком роботи встановимо PyTorch. Вибір версії залежить від наявності GPU:",[5367,5368,5369,5405,5433,5457],"tabs",{},[5370,5371,5373],"tabs-item",{"label":5372},"pip (CPU)",[5374,5375,5380],"pre",{"className":5376,"code":5377,"language":5378,"meta":5379,"style":5379},"language-bash shiki shiki-themes light-plus dark-plus dark-plus","pip install torch torchvision torchaudio\n","bash","",[5381,5382,5383],"code",{"__ignoreMap":5379},[5384,5385,5388,5392,5396,5399,5402],"span",{"class":5386,"line":5387},"line",1,[5384,5389,5391],{"class":5390},"s8Opu","pip",[5384,5393,5395],{"class":5394},"sbdoH"," install",[5384,5397,5398],{"class":5394}," torch",[5384,5400,5401],{"class":5394}," torchvision",[5384,5403,5404],{"class":5394}," torchaudio\n",[5370,5406,5408],{"label":5407},"pip (CUDA 11.8)",[5374,5409,5411],{"className":5376,"code":5410,"language":5378,"meta":5379,"style":5379},"pip install torch torchvision torchaudio --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu118\n",[5381,5412,5413],{"__ignoreMap":5379},[5384,5414,5415,5417,5419,5421,5423,5426,5430],{"class":5386,"line":5387},[5384,5416,5391],{"class":5390},[5384,5418,5395],{"class":5394},[5384,5420,5398],{"class":5394},[5384,5422,5401],{"class":5394},[5384,5424,5425],{"class":5394}," torchaudio",[5384,5427,5429],{"class":5428},"su1O8"," --index-url",[5384,5431,5432],{"class":5394}," https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu118\n",[5370,5434,5436],{"label":5435},"uv (CPU)",[5374,5437,5439],{"className":5376,"code":5438,"language":5378,"meta":5379,"style":5379},"uv pip install torch torchvision torchaudio\n",[5381,5440,5441],{"__ignoreMap":5379},[5384,5442,5443,5446,5449,5451,5453,5455],{"class":5386,"line":5387},[5384,5444,5445],{"class":5390},"uv",[5384,5447,5448],{"class":5394}," pip",[5384,5450,5395],{"class":5394},[5384,5452,5398],{"class":5394},[5384,5454,5401],{"class":5394},[5384,5456,5404],{"class":5394},[5370,5458,5460],{"label":5459},"poetry",[5374,5461,5463],{"className":5376,"code":5462,"language":5378,"meta":5379,"style":5379},"poetry add torch torchvision torchaudio\n",[5381,5464,5465],{"__ignoreMap":5379},[5384,5466,5467,5469,5472,5474,5476],{"class":5386,"line":5387},[5384,5468,5459],{"class":5390},[5384,5470,5471],{"class":5394}," add",[5384,5473,5398],{"class":5394},[5384,5475,5401],{"class":5394},[5384,5477,5404],{"class":5394},[5479,5480,5481,5482,5485],"note",{},"Якщо у вас є NVIDIA GPU, обов'язково встановіть версію з підтримкою CUDA — це дасть прискорення у 20-50 разів. Перевірити наявність GPU можна командою ",[5381,5483,5484],{},"nvidia-smi"," у терміналі.",[5262,5487,5488],{},"Перевіримо, що PyTorch встановлено коректно:",[5490,5491,5494,5614],"jupyter-notebook",{"title":5492,"execution-count":5493},"pytorch_check.ipynb","1",[5374,5495,5499],{"className":5496,"code":5497,"language":5498,"meta":5379,"style":5379},"language-python shiki shiki-themes light-plus dark-plus dark-plus","import torch\n\nprint(f\"PyTorch версія: {torch.__version__}\")\nprint(f\"CUDA доступна: {torch.cuda.is_available()}\")\nif torch.cuda.is_available():\n    print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n","python",[5381,5500,5501,5511,5518,5552,5575,5584],{"__ignoreMap":5379},[5384,5502,5503,5507],{"class":5386,"line":5387},[5384,5504,5506],{"class":5505},"s8xlr","import",[5384,5508,5510],{"class":5509},"sHH4Y"," torch\n",[5384,5512,5514],{"class":5386,"line":5513},2,[5384,5515,5517],{"emptyLinePlaceholder":5516},true,"\n",[5384,5519,5521,5524,5527,5530,5533,5536,5539,5543,5546,5549],{"class":5386,"line":5520},3,[5384,5522,5523],{"class":5390},"print",[5384,5525,5526],{"class":5509},"(",[5384,5528,5529],{"class":5428},"f",[5384,5531,5532],{"class":5394},"\"PyTorch версія: ",[5384,5534,5535],{"class":5428},"{",[5384,5537,5538],{"class":5509},"torch.",[5384,5540,5542],{"class":5541},"siwwj","__version__",[5384,5544,5545],{"class":5428},"}",[5384,5547,5548],{"class":5394},"\"",[5384,5550,5551],{"class":5509},")\n",[5384,5553,5555,5557,5559,5561,5564,5566,5569,5571,5573],{"class":5386,"line":5554},4,[5384,5556,5523],{"class":5390},[5384,5558,5526],{"class":5509},[5384,5560,5529],{"class":5428},[5384,5562,5563],{"class":5394},"\"CUDA доступна: ",[5384,5565,5535],{"class":5428},[5384,5567,5568],{"class":5509},"torch.cuda.is_available()",[5384,5570,5545],{"class":5428},[5384,5572,5548],{"class":5394},[5384,5574,5551],{"class":5509},[5384,5576,5578,5581],{"class":5386,"line":5577},5,[5384,5579,5580],{"class":5505},"if",[5384,5582,5583],{"class":5509}," torch.cuda.is_available():\n",[5384,5585,5587,5590,5592,5594,5597,5599,5602,5606,5608,5610,5612],{"class":5386,"line":5586},6,[5384,5588,5589],{"class":5390},"    print",[5384,5591,5526],{"class":5509},[5384,5593,5529],{"class":5428},[5384,5595,5596],{"class":5394},"\"GPU: ",[5384,5598,5535],{"class":5428},[5384,5600,5601],{"class":5509},"torch.cuda.get_device_name(",[5384,5603,5605],{"class":5604},"sJj4R","0",[5384,5607,5227],{"class":5509},[5384,5609,5545],{"class":5428},[5384,5611,5548],{"class":5394},[5384,5613,5551],{"class":5509},[5615,5616,5617],"template",{"v-slot:output":5379},[5618,5619,5620,5638],"table",{},[5621,5622,5623],"thead",{},[5624,5625,5626,5631,5634],"tr",{},[5627,5628,5630],"th",{"align":5629},"left","Параметр системи",[5627,5632,5633],{"align":5629},"Значення",[5627,5635,5637],{"align":5636},"center","Статус",[5639,5640,5641,5657,5672],"tbody",{},[5624,5642,5643,5649,5654],{},[5644,5645,5646],"td",{"align":5629},[5220,5647,5648],{},"PyTorch версія",[5644,5650,5651],{"align":5629},[5381,5652,5653],{},"2.1.0",[5644,5655,5656],{"align":5636},"✅ Встановлено",[5624,5658,5659,5664,5669],{},[5644,5660,5661],{"align":5629},[5220,5662,5663],{},"CUDA доступна",[5644,5665,5666],{"align":5629},[5381,5667,5668],{},"True",[5644,5670,5671],{"align":5636},"⚡ Активовано",[5624,5673,5674,5679,5684],{},[5644,5675,5676],{"align":5629},[5220,5677,5678],{},"Апаратний прискорювач (GPU)",[5644,5680,5681],{"align":5629},[5381,5682,5683],{},"NVIDIA GeForce RTX 3080",[5644,5685,5686],{"align":5636},"🚀 Готово до обчислень",[5688,5689],"hr",{},[5257,5691,5693],{"id":5692},"частина-1-тензори-основа-pytorch","Частина 1: Тензори — основа PyTorch",[5695,5696,5698],"h3",{"id":5697},"що-таке-тензор","Що таке тензор?",[5262,5700,5701,5705],{},[5220,5702,5222,5703,5227],{},[5224,5704,5226],{}," — це багатовимірний масив чисел, основна структура даних у PyTorch. Тензори схожі на NumPy масиви, але мають додаткові можливості: можуть виконуватися на GPU та автоматично відстежувати операції для обчислення градієнтів.",[5351,5707,5708],{},[5262,5709,5710,5712],{},[5220,5711,5357],{}," Тензор — це як універсальний контейнер для даних. Скаляр (одне число) — тензор 0-го рангу, вектор (список чисел) — тензор 1-го рангу, матриця (таблиця) — тензор 2-го рангу, а зображення RGB (висота × ширина × 3 канали кольору) — тензор 3-го рангу.",[5262,5714,5715],{},[5268,5716],{"alt":5717,"className":5718,"src":5719},"Геометрична ієрархія розмірностей тензорів: від 0D скаляра до 4D батчу",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F02.png",[5275,5721,5723,5725,5728,5731,5756,5759],{"alt":5722},"Ієрархія розмірностей тензорів у PyTorch",[5262,5724,5280],{},[5262,5726,5727],{},"skinparam state {\nBackgroundColor #F8FAFC\nBorderColor #94A3B8\nFontColor #0F172A\nFontSize 12\nArrowColor #64748B\n}",[5262,5729,5730],{},"title Ієрархія рангів тензорів: від скаляра до пакетів зображень",[5262,5732,5733,5734,5736,5737,5740,5741,5744,5745,5748,5749,5752,5753,5227],{},"state \"0D: Скаляр (torch.tensor(42))\" as D0 #EFF6FF : Одне число без осей (shape: ",[5384,5735],{},")\nstate \"1D: Вектор (torch.tensor(",[5384,5738,5739],{},"1, 2, 3","))\" as D1 #F0FDF4 : Одновимірний масив (shape: ",[5384,5742,5743],{},"3",")\nstate \"2D: Матриця (torch.zeros(3, 3))\" as D2 #FEF3C7 : Рядки та стовпці (shape: ",[5384,5746,5747],{},"3, 3",")\nstate \"3D: Об'ємний тензор (Зображення RGB)\" as D3 #FEE2E2 : Канали x Висота x Ширина (shape: ",[5384,5750,5751],{},"3, H, W",")\nstate \"4D: Батч тензорів (Пакет зображень)\" as D4 #F3E8FF : Батч x Канали x Висота x Ширина (shape: ",[5384,5754,5755],{},"B, C, H, W",[5262,5757,5758],{},"D0 -down-> D1 : Додавання осі виміру\nD1 -down-> D2 : Додавання площини\nD2 -down-> D3 : Додавання глибини (RGB канали)\nD3 -down-> D4 : Об'єднання у пакет (Batch)",[5262,5760,5292],{},[5186,5762,5763,5861],{},[5189,5764,5766],{"icon":943,"title":5765},"📊 Ранги тензорів",[5618,5767,5768,5781],{},[5621,5769,5770],{},[5624,5771,5772,5775,5778],{},[5627,5773,5774],{"align":5629},"Ранг",[5627,5776,5777],{"align":5629},"Форма",[5627,5779,5780],{"align":5629},"Приклад використання",[5639,5782,5783,5796,5809,5822,5835,5848],{},[5624,5784,5785,5788,5793],{},[5644,5786,5787],{"align":5629},"0D",[5644,5789,5790],{"align":5629},[5381,5791,5792],{},"()",[5644,5794,5795],{"align":5629},"Скаляр: температура, вік",[5624,5797,5798,5801,5806],{},[5644,5799,5800],{"align":5629},"1D",[5644,5802,5803],{"align":5629},[5381,5804,5805],{},"(n,)",[5644,5807,5808],{"align":5629},"Вектор: ціни, оцінки",[5624,5810,5811,5814,5819],{},[5644,5812,5813],{"align":5629},"2D",[5644,5815,5816],{"align":5629},[5381,5817,5818],{},"(n, m)",[5644,5820,5821],{"align":5629},"Матриця: таблиця даних",[5624,5823,5824,5827,5832],{},[5644,5825,5826],{"align":5629},"3D",[5644,5828,5829],{"align":5629},[5381,5830,5831],{},"(h, w, c)",[5644,5833,5834],{"align":5629},"Зображення RGB",[5624,5836,5837,5840,5845],{},[5644,5838,5839],{"align":5629},"4D",[5644,5841,5842],{"align":5629},[5381,5843,5844],{},"(b, c, h, w)",[5644,5846,5847],{"align":5629},"Батч зображень",[5624,5849,5850,5853,5858],{},[5644,5851,5852],{"align":5629},"5D",[5644,5854,5855],{"align":5629},[5381,5856,5857],{},"(b, c, d, h, w)",[5644,5859,5860],{"align":5629},"Батч 3D-сканів (МРТ, КТ)",[5189,5862,5865],{"icon":5863,"title":5864},"i-lucide-git-compare","🔄 NumPy vs PyTorch",[5618,5866,5867,5880],{},[5621,5868,5869],{},[5624,5870,5871,5874,5877],{},[5627,5872,5873],{"align":5629},"Операція",[5627,5875,5876],{"align":5629},"NumPy",[5627,5878,5879],{"align":5629},"PyTorch",[5639,5881,5882,5897,5912,5927,5941,5956],{},[5624,5883,5884,5887,5892],{},[5644,5885,5886],{"align":5629},"Створення масиву",[5644,5888,5889],{"align":5629},[5381,5890,5891],{},"np.array([1,2])",[5644,5893,5894],{"align":5629},[5381,5895,5896],{},"torch.tensor([1,2])",[5624,5898,5899,5902,5907],{},[5644,5900,5901],{"align":5629},"Нулі",[5644,5903,5904],{"align":5629},[5381,5905,5906],{},"np.zeros((3,3))",[5644,5908,5909],{"align":5629},[5381,5910,5911],{},"torch.zeros(3,3)",[5624,5913,5914,5917,5922],{},[5644,5915,5916],{"align":5629},"Випадкові числа",[5644,5918,5919],{"align":5629},[5381,5920,5921],{},"np.random.rand()",[5644,5923,5924],{"align":5629},[5381,5925,5926],{},"torch.rand()",[5624,5928,5929,5931,5936],{},[5644,5930,5777],{"align":5629},[5644,5932,5933],{"align":5629},[5381,5934,5935],{},"arr.shape",[5644,5937,5938],{"align":5629},[5381,5939,5940],{},"tensor.shape",[5624,5942,5943,5946,5951],{},[5644,5944,5945],{"align":5629},"Зміна форми",[5644,5947,5948],{"align":5629},[5381,5949,5950],{},"arr.reshape()",[5644,5952,5953],{"align":5629},[5381,5954,5955],{},"tensor.view()",[5624,5957,5958,5961,5966],{},[5644,5959,5960],{"align":5629},"Транспонування",[5644,5962,5963],{"align":5629},[5381,5964,5965],{},"arr.T",[5644,5967,5968],{"align":5629},[5381,5969,5970],{},"tensor.T",[5695,5972,5974],{"id":5973},"створення-тензорів","Створення тензорів",[5262,5976,5977],{},"Розглянемо основні способи створення тензорів у PyTorch:",[5979,5980,5982],"h4",{"id":5981},"_1-з-python-списків","1. З Python-списків",[5490,5984,5986,6167,6366],{"title":5985},"tensor_from_list.ipynb",[5987,5988,5989,6107],"jupyter-cell",{"execution-count":5493},[5374,5990,5992],{"className":5496,"code":5991,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Одновимірний тензор (вектор)\nvector = torch.tensor([1, 2, 3, 4, 5])\nprint(f\"Вектор: {vector}\")\nprint(f\"Форма: {vector.shape}\")\nprint(f\"Ранг: {vector.ndim}\")\n",[5381,5993,5994,6000,6004,6010,6040,6062,6084],{"__ignoreMap":5379},[5384,5995,5996,5998],{"class":5386,"line":5387},[5384,5997,5506],{"class":5505},[5384,5999,5510],{"class":5509},[5384,6001,6002],{"class":5386,"line":5513},[5384,6003,5517],{"emptyLinePlaceholder":5516},[5384,6005,6006],{"class":5386,"line":5520},[5384,6007,6009],{"class":6008},"spJ8K","# Одновимірний тензор (вектор)\n",[5384,6011,6012,6015,6017,6020,6023,6025,6027,6029,6032,6034,6037],{"class":5386,"line":5554},[5384,6013,6014],{"class":5509},"vector = torch.tensor([",[5384,6016,5493],{"class":5604},[5384,6018,6019],{"class":5509},", ",[5384,6021,6022],{"class":5604},"2",[5384,6024,6019],{"class":5509},[5384,6026,5743],{"class":5604},[5384,6028,6019],{"class":5509},[5384,6030,6031],{"class":5604},"4",[5384,6033,6019],{"class":5509},[5384,6035,6036],{"class":5604},"5",[5384,6038,6039],{"class":5509},"])\n",[5384,6041,6042,6044,6046,6048,6051,6053,6056,6058,6060],{"class":5386,"line":5577},[5384,6043,5523],{"class":5390},[5384,6045,5526],{"class":5509},[5384,6047,5529],{"class":5428},[5384,6049,6050],{"class":5394},"\"Вектор: ",[5384,6052,5535],{"class":5428},[5384,6054,6055],{"class":5509},"vector",[5384,6057,5545],{"class":5428},[5384,6059,5548],{"class":5394},[5384,6061,5551],{"class":5509},[5384,6063,6064,6066,6068,6070,6073,6075,6078,6080,6082],{"class":5386,"line":5586},[5384,6065,5523],{"class":5390},[5384,6067,5526],{"class":5509},[5384,6069,5529],{"class":5428},[5384,6071,6072],{"class":5394},"\"Форма: ",[5384,6074,5535],{"class":5428},[5384,6076,6077],{"class":5509},"vector.shape",[5384,6079,5545],{"class":5428},[5384,6081,5548],{"class":5394},[5384,6083,5551],{"class":5509},[5384,6085,6087,6089,6091,6093,6096,6098,6101,6103,6105],{"class":5386,"line":6086},7,[5384,6088,5523],{"class":5390},[5384,6090,5526],{"class":5509},[5384,6092,5529],{"class":5428},[5384,6094,6095],{"class":5394},"\"Ранг: ",[5384,6097,5535],{"class":5428},[5384,6099,6100],{"class":5509},"vector.ndim",[5384,6102,5545],{"class":5428},[5384,6104,5548],{"class":5394},[5384,6106,5551],{"class":5509},[5615,6108,6109],{"v-slot:output":5379},[5618,6110,6111,6139],{},[5621,6112,6113],{},[5624,6114,6115,6118,6121,6127,6133],{},[5627,6116,6117],{"align":5629},"Об'єкт",[5627,6119,6120],{"align":5629},"Значення тензора",[5627,6122,6123,6124,5227],{"align":5636},"Форма (",[5381,6125,6126],{},"shape",[5627,6128,6129,6130,5227],{"align":5636},"Ранг (",[5381,6131,6132],{},"ndim",[5627,6134,6135,6136,5227],{"align":5636},"Тип даних (",[5381,6137,6138],{},"dtype",[5639,6140,6141],{},[5624,6142,6143,6148,6153,6158,6162],{},[5644,6144,6145],{"align":5629},[5220,6146,6147],{},"Вектор",[5644,6149,6150],{"align":5629},[5381,6151,6152],{},"tensor([1, 2, 3, 4, 5])",[5644,6154,6155],{"align":5636},[5381,6156,6157],{},"torch.Size([5])",[5644,6159,6160],{"align":5636},[5381,6161,5493],{},[5644,6163,6164],{"align":5636},[5381,6165,6166],{},"torch.int64",[5987,6168,6169,6293],{"execution-count":6022},[5374,6170,6172],{"className":5496,"code":6171,"language":5498,"meta":5379,"style":5379},"# Двовимірний тензор (матриця)\nmatrix = torch.tensor([\n    [1, 2, 3],\n    [4, 5, 6]\n])\nprint(f\"Матриця:\\n{matrix}\")\nprint(f\"Форма: {matrix.shape}\")\nprint(f\"Ранг: {matrix.ndim}\")\n",[5381,6173,6174,6179,6184,6202,6220,6224,6250,6271],{"__ignoreMap":5379},[5384,6175,6176],{"class":5386,"line":5387},[5384,6177,6178],{"class":6008},"# Двовимірний тензор (матриця)\n",[5384,6180,6181],{"class":5386,"line":5513},[5384,6182,6183],{"class":5509},"matrix = torch.tensor([\n",[5384,6185,6186,6189,6191,6193,6195,6197,6199],{"class":5386,"line":5520},[5384,6187,6188],{"class":5509},"    [",[5384,6190,5493],{"class":5604},[5384,6192,6019],{"class":5509},[5384,6194,6022],{"class":5604},[5384,6196,6019],{"class":5509},[5384,6198,5743],{"class":5604},[5384,6200,6201],{"class":5509},"],\n",[5384,6203,6204,6206,6208,6210,6212,6214,6217],{"class":5386,"line":5554},[5384,6205,6188],{"class":5509},[5384,6207,6031],{"class":5604},[5384,6209,6019],{"class":5509},[5384,6211,6036],{"class":5604},[5384,6213,6019],{"class":5509},[5384,6215,6216],{"class":5604},"6",[5384,6218,6219],{"class":5509},"]\n",[5384,6221,6222],{"class":5386,"line":5577},[5384,6223,6039],{"class":5509},[5384,6225,6226,6228,6230,6232,6235,6239,6241,6244,6246,6248],{"class":5386,"line":5586},[5384,6227,5523],{"class":5390},[5384,6229,5526],{"class":5509},[5384,6231,5529],{"class":5428},[5384,6233,6234],{"class":5394},"\"Матриця:",[5384,6236,6238],{"class":6237},"sjcCO","\\n",[5384,6240,5535],{"class":5428},[5384,6242,6243],{"class":5509},"matrix",[5384,6245,5545],{"class":5428},[5384,6247,5548],{"class":5394},[5384,6249,5551],{"class":5509},[5384,6251,6252,6254,6256,6258,6260,6262,6265,6267,6269],{"class":5386,"line":6086},[5384,6253,5523],{"class":5390},[5384,6255,5526],{"class":5509},[5384,6257,5529],{"class":5428},[5384,6259,6072],{"class":5394},[5384,6261,5535],{"class":5428},[5384,6263,6264],{"class":5509},"matrix.shape",[5384,6266,5545],{"class":5428},[5384,6268,5548],{"class":5394},[5384,6270,5551],{"class":5509},[5384,6272,6274,6276,6278,6280,6282,6284,6287,6289,6291],{"class":5386,"line":6273},8,[5384,6275,5523],{"class":5390},[5384,6277,5526],{"class":5509},[5384,6279,5529],{"class":5428},[5384,6281,6095],{"class":5394},[5384,6283,5535],{"class":5428},[5384,6285,6286],{"class":5509},"matrix.ndim",[5384,6288,5545],{"class":5428},[5384,6290,5548],{"class":5394},[5384,6292,5551],{"class":5509},[5615,6294,6295],{"v-slot:output":5379},[5618,6296,6297,6316],{},[5621,6298,6299],{},[5624,6300,6301,6304,6307,6310,6313],{},[5627,6302,6303],{"align":5636},"Рядок матриці",[5627,6305,6306],{"align":5636},"Стовпчик 0",[5627,6308,6309],{"align":5636},"Стовпчик 1",[5627,6311,6312],{"align":5636},"Стовпчик 2",[5627,6314,6315],{"align":5636},"Форма \u002F Ранг",[5639,6317,6318,6342],{},[5624,6319,6320,6325,6329,6333,6337],{},[5644,6321,6322],{"align":5636},[5220,6323,6324],{},"Рядок 0",[5644,6326,6327],{"align":5636},[5381,6328,5493],{},[5644,6330,6331],{"align":5636},[5381,6332,6022],{},[5644,6334,6335],{"align":5636},[5381,6336,5743],{},[5644,6338,6339],{"align":5636},[5381,6340,6341],{},"torch.Size([2, 3])",[5624,6343,6344,6349,6353,6357,6361],{},[5644,6345,6346],{"align":5636},[5220,6347,6348],{},"Рядок 1",[5644,6350,6351],{"align":5636},[5381,6352,6031],{},[5644,6354,6355],{"align":5636},[5381,6356,6036],{},[5644,6358,6359],{"align":5636},[5381,6360,6216],{},[5644,6362,6363,6364],{"align":5636},"Ранг: ",[5381,6365,6022],{},[5987,6367,6368,6503],{"execution-count":5743},[5374,6369,6371],{"className":5496,"code":6370,"language":5498,"meta":5379,"style":5379},"# Тривимірний тензор (наприклад, RGB зображення 2×2 пікселі)\nimage = torch.tensor([\n    [[255, 0, 0], [0, 255, 0]],      # перший рядок пікселів\n    [[0, 0, 255], [255, 255, 0]]     # другий рядок пікселів\n])\nprint(f\"Зображення:\\n{image}\")\nprint(f\"Форма: {image.shape}\")  # (висота, ширина, канали)\n",[5381,6372,6373,6378,6383,6418,6450,6454,6478],{"__ignoreMap":5379},[5384,6374,6375],{"class":5386,"line":5387},[5384,6376,6377],{"class":6008},"# Тривимірний тензор (наприклад, RGB зображення 2×2 пікселі)\n",[5384,6379,6380],{"class":5386,"line":5513},[5384,6381,6382],{"class":5509},"image = torch.tensor([\n",[5384,6384,6385,6388,6391,6393,6395,6397,6399,6402,6404,6406,6408,6410,6412,6415],{"class":5386,"line":5520},[5384,6386,6387],{"class":5509},"    [[",[5384,6389,6390],{"class":5604},"255",[5384,6392,6019],{"class":5509},[5384,6394,5605],{"class":5604},[5384,6396,6019],{"class":5509},[5384,6398,5605],{"class":5604},[5384,6400,6401],{"class":5509},"], [",[5384,6403,5605],{"class":5604},[5384,6405,6019],{"class":5509},[5384,6407,6390],{"class":5604},[5384,6409,6019],{"class":5509},[5384,6411,5605],{"class":5604},[5384,6413,6414],{"class":5509},"]],      ",[5384,6416,6417],{"class":6008},"# перший рядок пікселів\n",[5384,6419,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6447],{"class":5386,"line":5554},[5384,6421,6387],{"class":5509},[5384,6423,5605],{"class":5604},[5384,6425,6019],{"class":5509},[5384,6427,5605],{"class":5604},[5384,6429,6019],{"class":5509},[5384,6431,6390],{"class":5604},[5384,6433,6401],{"class":5509},[5384,6435,6390],{"class":5604},[5384,6437,6019],{"class":5509},[5384,6439,6390],{"class":5604},[5384,6441,6019],{"class":5509},[5384,6443,5605],{"class":5604},[5384,6445,6446],{"class":5509},"]]     ",[5384,6448,6449],{"class":6008},"# другий рядок пікселів\n",[5384,6451,6452],{"class":5386,"line":5577},[5384,6453,6039],{"class":5509},[5384,6455,6456,6458,6460,6462,6465,6467,6469,6472,6474,6476],{"class":5386,"line":5586},[5384,6457,5523],{"class":5390},[5384,6459,5526],{"class":5509},[5384,6461,5529],{"class":5428},[5384,6463,6464],{"class":5394},"\"Зображення:",[5384,6466,6238],{"class":6237},[5384,6468,5535],{"class":5428},[5384,6470,6471],{"class":5509},"image",[5384,6473,5545],{"class":5428},[5384,6475,5548],{"class":5394},[5384,6477,5551],{"class":5509},[5384,6479,6480,6482,6484,6486,6488,6490,6493,6495,6497,6500],{"class":5386,"line":6086},[5384,6481,5523],{"class":5390},[5384,6483,5526],{"class":5509},[5384,6485,5529],{"class":5428},[5384,6487,6072],{"class":5394},[5384,6489,5535],{"class":5428},[5384,6491,6492],{"class":5509},"image.shape",[5384,6494,5545],{"class":5428},[5384,6496,5548],{"class":5394},[5384,6498,6499],{"class":5509},")  ",[5384,6501,6502],{"class":6008},"# (висота, ширина, канали)\n",[5615,6504,6505],{"v-slot:output":5379},[5618,6506,6507,6527],{},[5621,6508,6509],{},[5624,6510,6511,6514,6517,6524],{},[5627,6512,6513],{"align":5629},"Канал кольору",[5627,6515,6516],{"align":5636},"Висота $\\times$ Ширина",[5627,6518,6519,6520,6523],{"align":5629},"Значення зрізу $",[5384,6521,6522],{},"2 \\times 3","$",[5627,6525,6526],{"align":5636},"Форма тензора",[5639,6528,6529,6549,6568],{},[5624,6530,6531,6536,6539,6544],{},[5644,6532,6533],{"align":5629},[5220,6534,6535],{},"Червоний (R)",[5644,6537,6538],{"align":5636},"$2 \\times 3$ пікселі",[5644,6540,6541],{"align":5629},[5381,6542,6543],{},"[[255, 0, 0], [0, 255, 0]]",[5644,6545,6546],{"align":5636},[5381,6547,6548],{},"torch.Size([3, 2, 3])",[5624,6550,6551,6556,6558,6563],{},[5644,6552,6553],{"align":5629},[5220,6554,6555],{},"Зелений (G)",[5644,6557,6538],{"align":5636},[5644,6559,6560],{"align":5629},[5381,6561,6562],{},"[[0, 255, 0], [255, 255, 0]]",[5644,6564,6363,6565,6567],{"align":5636},[5381,6566,5743],{}," (3D Tensor)",[5624,6569,6570,6575,6577,6582],{},[5644,6571,6572],{"align":5629},[5220,6573,6574],{},"Синій (B)",[5644,6576,6538],{"align":5636},[5644,6578,6579],{"align":5629},[5381,6580,6581],{},"[[255, 0, 255], [0, 255, 255]]",[5644,6583,6584,6585],{"align":5636},"Тип: ",[5381,6586,6166],{},[6588,6589,6590,6591,6594,6595,6598,6599,6602],"tip",{},"Для створення тензорів завжди використовуйте ",[5381,6592,6593],{},"torch.tensor()",", а не ",[5381,6596,6597],{},"torch.Tensor()",". Перший створює тензор із автоматичним визначенням типу даних, другий завжди створює тензор типу ",[5381,6600,6601],{},"float32",", що може призвести до неочікуваних помилок.",[5979,6604,6606],{"id":6605},"_2-з-numpy-масивів","2. З NumPy масивів",[5262,6608,6609],{},"PyTorch має відмінну інтеграцію з NumPy — можна легко конвертувати масиви в обидва боки:",[5490,6611,6613,6852],{"title":6612},"numpy_to_torch.ipynb",[5987,6614,6615,6781],{"execution-count":5493},[5374,6616,6618],{"className":5496,"code":6617,"language":5498,"meta":5379,"style":5379},"import numpy as np\nimport torch\n\n# NumPy масив → PyTorch тензор\nnp_array = np.array([[1, 2, 3], [4, 5, 6]])\ntensor = torch.from_numpy(np_array)\n\nprint(f\"NumPy масив:\\n{np_array}\")\nprint(f\"PyTorch тензор:\\n{tensor}\")\nprint(f\"Тип даних NumPy: {np_array.dtype}\")\nprint(f\"Тип даних PyTorch: {tensor.dtype}\")\n",[5381,6619,6620,6633,6639,6643,6648,6678,6683,6687,6711,6735,6758],{"__ignoreMap":5379},[5384,6621,6622,6624,6627,6630],{"class":5386,"line":5387},[5384,6623,5506],{"class":5505},[5384,6625,6626],{"class":5509}," numpy ",[5384,6628,6629],{"class":5505},"as",[5384,6631,6632],{"class":5509}," np\n",[5384,6634,6635,6637],{"class":5386,"line":5513},[5384,6636,5506],{"class":5505},[5384,6638,5510],{"class":5509},[5384,6640,6641],{"class":5386,"line":5520},[5384,6642,5517],{"emptyLinePlaceholder":5516},[5384,6644,6645],{"class":5386,"line":5554},[5384,6646,6647],{"class":6008},"# NumPy масив → PyTorch тензор\n",[5384,6649,6650,6653,6655,6657,6659,6661,6663,6665,6667,6669,6671,6673,6675],{"class":5386,"line":5577},[5384,6651,6652],{"class":5509},"np_array = np.array([[",[5384,6654,5493],{"class":5604},[5384,6656,6019],{"class":5509},[5384,6658,6022],{"class":5604},[5384,6660,6019],{"class":5509},[5384,6662,5743],{"class":5604},[5384,6664,6401],{"class":5509},[5384,6666,6031],{"class":5604},[5384,6668,6019],{"class":5509},[5384,6670,6036],{"class":5604},[5384,6672,6019],{"class":5509},[5384,6674,6216],{"class":5604},[5384,6676,6677],{"class":5509},"]])\n",[5384,6679,6680],{"class":5386,"line":5586},[5384,6681,6682],{"class":5509},"tensor = torch.from_numpy(np_array)\n",[5384,6684,6685],{"class":5386,"line":6086},[5384,6686,5517],{"emptyLinePlaceholder":5516},[5384,6688,6689,6691,6693,6695,6698,6700,6702,6705,6707,6709],{"class":5386,"line":6273},[5384,6690,5523],{"class":5390},[5384,6692,5526],{"class":5509},[5384,6694,5529],{"class":5428},[5384,6696,6697],{"class":5394},"\"NumPy масив:",[5384,6699,6238],{"class":6237},[5384,6701,5535],{"class":5428},[5384,6703,6704],{"class":5509},"np_array",[5384,6706,5545],{"class":5428},[5384,6708,5548],{"class":5394},[5384,6710,5551],{"class":5509},[5384,6712,6714,6716,6718,6720,6723,6725,6727,6729,6731,6733],{"class":5386,"line":6713},9,[5384,6715,5523],{"class":5390},[5384,6717,5526],{"class":5509},[5384,6719,5529],{"class":5428},[5384,6721,6722],{"class":5394},"\"PyTorch тензор:",[5384,6724,6238],{"class":6237},[5384,6726,5535],{"class":5428},[5384,6728,5226],{"class":5509},[5384,6730,5545],{"class":5428},[5384,6732,5548],{"class":5394},[5384,6734,5551],{"class":5509},[5384,6736,6738,6740,6742,6744,6747,6749,6752,6754,6756],{"class":5386,"line":6737},10,[5384,6739,5523],{"class":5390},[5384,6741,5526],{"class":5509},[5384,6743,5529],{"class":5428},[5384,6745,6746],{"class":5394},"\"Тип даних NumPy: ",[5384,6748,5535],{"class":5428},[5384,6750,6751],{"class":5509},"np_array.dtype",[5384,6753,5545],{"class":5428},[5384,6755,5548],{"class":5394},[5384,6757,5551],{"class":5509},[5384,6759,6761,6763,6765,6767,6770,6772,6775,6777,6779],{"class":5386,"line":6760},11,[5384,6762,5523],{"class":5390},[5384,6764,5526],{"class":5509},[5384,6766,5529],{"class":5428},[5384,6768,6769],{"class":5394},"\"Тип даних PyTorch: ",[5384,6771,5535],{"class":5428},[5384,6773,6774],{"class":5509},"tensor.dtype",[5384,6776,5545],{"class":5428},[5384,6778,5548],{"class":5394},[5384,6780,5551],{"class":5509},[5615,6782,6783],{"v-slot:output":5379},[5618,6784,6785,6807],{},[5621,6786,6787],{},[5624,6788,6789,6792,6798,6804],{},[5627,6790,6791],{"align":5629},"Стан об'єктів",[5627,6793,6794,6795,5227],{"align":5629},"NumPy масив (",[5381,6796,6797],{},"np.ndarray",[5627,6799,6800,6801,5227],{"align":5629},"PyTorch тензор (",[5381,6802,6803],{},"from_numpy",[5627,6805,6806],{"align":5636},"Спільна пам'ять (Zero-copy)",[5639,6808,6809,6829],{},[5624,6810,6811,6816,6821,6826],{},[5644,6812,6813],{"align":5629},[5220,6814,6815],{},"Початковий стан",[5644,6817,6818],{"align":5629},[5381,6819,6820],{},"[[1, 2, 3], [4, 5, 6]]",[5644,6822,6823],{"align":5629},[5381,6824,6825],{},"tensor([[1, 2, 3], [4, 5, 6]])",[5644,6827,6828],{"align":5636},"✅ Спільний буфер RAM",[5624,6830,6831,6839,6844,6849],{},[5644,6832,6833],{"align":5629},[5220,6834,6835,6836],{},"Після модифікації ",[5381,6837,6838],{},"np[0,0]=99",[5644,6840,6841],{"align":5629},[5381,6842,6843],{},"[[99, 2, 3], [4, 5, 6]]",[5644,6845,6846],{"align":5629},[5381,6847,6848],{},"tensor([[99, 2, 3], [4, 5, 6]])",[5644,6850,6851],{"align":5636},"⚡ Миттєва синхронізація",[5987,6853,6854,6942],{"execution-count":6022},[5374,6855,6857],{"className":5496,"code":6856,"language":5498,"meta":5379,"style":5379},"# PyTorch тензор → NumPy масив\ntensor = torch.tensor([[1.5, 2.5], [3.5, 4.5]])\nnp_array = tensor.numpy()\n\nprint(f\"PyTorch тензор:\\n{tensor}\")\nprint(f\"NumPy масив:\\n{np_array}\")\n",[5381,6858,6859,6864,6889,6894,6898,6920],{"__ignoreMap":5379},[5384,6860,6861],{"class":5386,"line":5387},[5384,6862,6863],{"class":6008},"# PyTorch тензор → NumPy масив\n",[5384,6865,6866,6869,6872,6874,6877,6879,6882,6884,6887],{"class":5386,"line":5513},[5384,6867,6868],{"class":5509},"tensor = torch.tensor([[",[5384,6870,6871],{"class":5604},"1.5",[5384,6873,6019],{"class":5509},[5384,6875,6876],{"class":5604},"2.5",[5384,6878,6401],{"class":5509},[5384,6880,6881],{"class":5604},"3.5",[5384,6883,6019],{"class":5509},[5384,6885,6886],{"class":5604},"4.5",[5384,6888,6677],{"class":5509},[5384,6890,6891],{"class":5386,"line":5520},[5384,6892,6893],{"class":5509},"np_array = tensor.numpy()\n",[5384,6895,6896],{"class":5386,"line":5554},[5384,6897,5517],{"emptyLinePlaceholder":5516},[5384,6899,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918],{"class":5386,"line":5577},[5384,6901,5523],{"class":5390},[5384,6903,5526],{"class":5509},[5384,6905,5529],{"class":5428},[5384,6907,6722],{"class":5394},[5384,6909,6238],{"class":6237},[5384,6911,5535],{"class":5428},[5384,6913,5226],{"class":5509},[5384,6915,5545],{"class":5428},[5384,6917,5548],{"class":5394},[5384,6919,5551],{"class":5509},[5384,6921,6922,6924,6926,6928,6930,6932,6934,6936,6938,6940],{"class":5386,"line":5586},[5384,6923,5523],{"class":5390},[5384,6925,5526],{"class":5509},[5384,6927,5529],{"class":5428},[5384,6929,6697],{"class":5394},[5384,6931,6238],{"class":6237},[5384,6933,5535],{"class":5428},[5384,6935,6704],{"class":5509},[5384,6937,5545],{"class":5428},[5384,6939,5548],{"class":5394},[5384,6941,5551],{"class":5509},[5615,6943,6944],{"v-slot:output":5379},[5618,6945,6946,6966],{},[5621,6947,6948],{},[5624,6949,6950,6953,6958,6963],{},[5627,6951,6952],{"align":5629},"Етап перетворення",[5627,6954,6800,6955,5227],{"align":5629},[5381,6956,6957],{},"torch.Tensor",[5627,6959,6794,6960,5227],{"align":5629},[5381,6961,6962],{},".numpy()",[5627,6964,6965],{"align":5636},"Результат зв'язку",[5639,6967,6968,6987],{},[5624,6969,6970,6975,6980,6984],{},[5644,6971,6972],{"align":5629},[5220,6973,6974],{},"Вихідний тензор",[5644,6976,6977],{"align":5629},[5381,6978,6979],{},"[[1.5, 2.5], [3.5, 4.5]]",[5644,6981,6982],{"align":5629},[5381,6983,6979],{},[5644,6985,6986],{"align":5636},"Спільна область пам'яті",[5624,6988,6989,6997,7002,7006],{},[5644,6990,6991],{"align":5629},[5220,6992,6993,6994],{},"Після ",[5381,6995,6996],{},"t[0,0] = 99.0",[5644,6998,6999],{"align":5629},[5381,7000,7001],{},"[[99.0, 2.5], [3.5, 4.5]]",[5644,7003,7004],{"align":5629},[5381,7005,7001],{},[5644,7007,7008],{"align":5636},"✅ Синхронна зміна масиву",[7010,7011,7012,7015,7016,7019,7020,7023,7024,7027,7028,7031],"warning",{},[5220,7013,7014],{},"Увага:"," ",[5381,7017,7018],{},"torch.from_numpy()"," створює тензор, що ",[5220,7021,7022],{},"ділить пам'ять"," з оригінальним NumPy масивом. Зміна одного з них автоматично змінить інший! Якщо потрібна незалежна копія, використовуйте ",[5381,7025,7026],{},"torch.tensor(np_array)"," або ",[5381,7029,7030],{},".clone()",".",[5979,7033,7035],{"id":7034},"_3-спеціальні-тензори","3. Спеціальні тензори",[5262,7037,7038],{},"PyTorch надає зручні функції для створення тензорів зі спеціальними значеннями:",[5490,7040,7042,7302],{"title":7041},"special_tensors.ipynb",[5987,7043,7044,7195],{"execution-count":5493},[5374,7045,7047],{"className":5496,"code":7046,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Тензор, заповнений нулями\nzeros = torch.zeros(3, 4)\nprint(f\"Нулі (3×4):\\n{zeros}\\n\")\n\n# Тензор, заповнений одиницями\nones = torch.ones(2, 3)\nprint(f\"Одиниці (2×3):\\n{ones}\\n\")\n\n# Одинична матриця (діагональ з одиниць)\nidentity = torch.eye(4)\nprint(f\"Одинична матриця 4×4:\\n{identity}\")\n",[5381,7048,7049,7055,7059,7064,7077,7103,7107,7112,7125,7151,7155,7160,7170],{"__ignoreMap":5379},[5384,7050,7051,7053],{"class":5386,"line":5387},[5384,7052,5506],{"class":5505},[5384,7054,5510],{"class":5509},[5384,7056,7057],{"class":5386,"line":5513},[5384,7058,5517],{"emptyLinePlaceholder":5516},[5384,7060,7061],{"class":5386,"line":5520},[5384,7062,7063],{"class":6008},"# Тензор, заповнений нулями\n",[5384,7065,7066,7069,7071,7073,7075],{"class":5386,"line":5554},[5384,7067,7068],{"class":5509},"zeros = torch.zeros(",[5384,7070,5743],{"class":5604},[5384,7072,6019],{"class":5509},[5384,7074,6031],{"class":5604},[5384,7076,5551],{"class":5509},[5384,7078,7079,7081,7083,7085,7088,7090,7092,7095,7097,7099,7101],{"class":5386,"line":5577},[5384,7080,5523],{"class":5390},[5384,7082,5526],{"class":5509},[5384,7084,5529],{"class":5428},[5384,7086,7087],{"class":5394},"\"Нулі (3×4):",[5384,7089,6238],{"class":6237},[5384,7091,5535],{"class":5428},[5384,7093,7094],{"class":5509},"zeros",[5384,7096,5545],{"class":5428},[5384,7098,6238],{"class":6237},[5384,7100,5548],{"class":5394},[5384,7102,5551],{"class":5509},[5384,7104,7105],{"class":5386,"line":5586},[5384,7106,5517],{"emptyLinePlaceholder":5516},[5384,7108,7109],{"class":5386,"line":6086},[5384,7110,7111],{"class":6008},"# Тензор, заповнений одиницями\n",[5384,7113,7114,7117,7119,7121,7123],{"class":5386,"line":6273},[5384,7115,7116],{"class":5509},"ones = torch.ones(",[5384,7118,6022],{"class":5604},[5384,7120,6019],{"class":5509},[5384,7122,5743],{"class":5604},[5384,7124,5551],{"class":5509},[5384,7126,7127,7129,7131,7133,7136,7138,7140,7143,7145,7147,7149],{"class":5386,"line":6713},[5384,7128,5523],{"class":5390},[5384,7130,5526],{"class":5509},[5384,7132,5529],{"class":5428},[5384,7134,7135],{"class":5394},"\"Одиниці (2×3):",[5384,7137,6238],{"class":6237},[5384,7139,5535],{"class":5428},[5384,7141,7142],{"class":5509},"ones",[5384,7144,5545],{"class":5428},[5384,7146,6238],{"class":6237},[5384,7148,5548],{"class":5394},[5384,7150,5551],{"class":5509},[5384,7152,7153],{"class":5386,"line":6737},[5384,7154,5517],{"emptyLinePlaceholder":5516},[5384,7156,7157],{"class":5386,"line":6760},[5384,7158,7159],{"class":6008},"# Одинична матриця (діагональ з одиниць)\n",[5384,7161,7163,7166,7168],{"class":5386,"line":7162},12,[5384,7164,7165],{"class":5509},"identity = torch.eye(",[5384,7167,6031],{"class":5604},[5384,7169,5551],{"class":5509},[5384,7171,7173,7175,7177,7179,7182,7184,7186,7189,7191,7193],{"class":5386,"line":7172},13,[5384,7174,5523],{"class":5390},[5384,7176,5526],{"class":5509},[5384,7178,5529],{"class":5428},[5384,7180,7181],{"class":5394},"\"Одинична матриця 4×4:",[5384,7183,6238],{"class":6237},[5384,7185,5535],{"class":5428},[5384,7187,7188],{"class":5509},"identity",[5384,7190,5545],{"class":5428},[5384,7192,5548],{"class":5394},[5384,7194,5551],{"class":5509},[5615,7196,7197],{"v-slot:output":5379},[5618,7198,7199,7215],{},[5621,7200,7201],{},[5624,7202,7203,7206,7209,7211],{},[5627,7204,7205],{"align":5629},"Функція ініціалізації",[5627,7207,7208],{"align":5629},"Отриманий тензор",[5627,7210,5777],{"align":5636},[5627,7212,6135,7213,5227],{"align":5636},[5381,7214,6138],{},[5639,7216,7217,7240,7261,7280],{},[5624,7218,7219,7224,7230,7235],{},[5644,7220,7221],{"align":5629},[5381,7222,7223],{},"torch.zeros(3, 4)",[5644,7225,7226,7227],{"align":5629},"Матриця $3 \\times 4$, заповнена нулями ",[5381,7228,7229],{},"0.0",[5644,7231,7232],{"align":5636},[5381,7233,7234],{},"torch.Size([3, 4])",[5644,7236,7237],{"align":5636},[5381,7238,7239],{},"torch.float32",[5624,7241,7242,7247,7253,7257],{},[5644,7243,7244],{"align":5629},[5381,7245,7246],{},"torch.ones(2, 3)",[5644,7248,7249,7250],{"align":5629},"Матриця $2 \\times 3$, заповнена одиницями ",[5381,7251,7252],{},"1.0",[5644,7254,7255],{"align":5636},[5381,7256,6341],{},[5644,7258,7259],{"align":5636},[5381,7260,7239],{},[5624,7262,7263,7268,7271,7276],{},[5644,7264,7265],{"align":5629},[5381,7266,7267],{},"torch.eye(3)",[5644,7269,7270],{"align":5629},"Одинична діагональна матриця $I_3$",[5644,7272,7273],{"align":5636},[5381,7274,7275],{},"torch.Size([3, 3])",[5644,7277,7278],{"align":5636},[5381,7279,7239],{},[5624,7281,7282,7287,7293,7298],{},[5644,7283,7284],{"align":5629},[5381,7285,7286],{},"torch.full((2, 2), 7)",[5644,7288,7289,7290],{"align":5629},"Матриця $2 \\times 2$, заповнена константою ",[5381,7291,7292],{},"7",[5644,7294,7295],{"align":5636},[5381,7296,7297],{},"torch.Size([2, 2])",[5644,7299,7300],{"align":5636},[5381,7301,6166],{},[5987,7303,7304,7410],{"execution-count":6022},[5374,7305,7307],{"className":5496,"code":7306,"language":5498,"meta":5379,"style":5379},"# Послідовність чисел (як range у Python)\nsequence = torch.arange(0, 10, 2)  # від 0 до 10 з кроком 2\nprint(f\"Послідовність: {sequence}\\n\")\n\n# Рівномірно розподілені числа (як linspace у NumPy)\nlinspace = torch.linspace(0, 1, 5)  # 5 чисел від 0 до 1\nprint(f\"Linspace: {linspace}\")\n",[5381,7308,7309,7314,7335,7359,7363,7368,7388],{"__ignoreMap":5379},[5384,7310,7311],{"class":5386,"line":5387},[5384,7312,7313],{"class":6008},"# Послідовність чисел (як range у Python)\n",[5384,7315,7316,7319,7321,7323,7326,7328,7330,7332],{"class":5386,"line":5513},[5384,7317,7318],{"class":5509},"sequence = torch.arange(",[5384,7320,5605],{"class":5604},[5384,7322,6019],{"class":5509},[5384,7324,7325],{"class":5604},"10",[5384,7327,6019],{"class":5509},[5384,7329,6022],{"class":5604},[5384,7331,6499],{"class":5509},[5384,7333,7334],{"class":6008},"# від 0 до 10 з кроком 2\n",[5384,7336,7337,7339,7341,7343,7346,7348,7351,7353,7355,7357],{"class":5386,"line":5520},[5384,7338,5523],{"class":5390},[5384,7340,5526],{"class":5509},[5384,7342,5529],{"class":5428},[5384,7344,7345],{"class":5394},"\"Послідовність: ",[5384,7347,5535],{"class":5428},[5384,7349,7350],{"class":5509},"sequence",[5384,7352,5545],{"class":5428},[5384,7354,6238],{"class":6237},[5384,7356,5548],{"class":5394},[5384,7358,5551],{"class":5509},[5384,7360,7361],{"class":5386,"line":5554},[5384,7362,5517],{"emptyLinePlaceholder":5516},[5384,7364,7365],{"class":5386,"line":5577},[5384,7366,7367],{"class":6008},"# Рівномірно розподілені числа (як linspace у NumPy)\n",[5384,7369,7370,7373,7375,7377,7379,7381,7383,7385],{"class":5386,"line":5586},[5384,7371,7372],{"class":5509},"linspace = torch.linspace(",[5384,7374,5605],{"class":5604},[5384,7376,6019],{"class":5509},[5384,7378,5493],{"class":5604},[5384,7380,6019],{"class":5509},[5384,7382,6036],{"class":5604},[5384,7384,6499],{"class":5509},[5384,7386,7387],{"class":6008},"# 5 чисел від 0 до 1\n",[5384,7389,7390,7392,7394,7396,7399,7401,7404,7406,7408],{"class":5386,"line":6086},[5384,7391,5523],{"class":5390},[5384,7393,5526],{"class":5509},[5384,7395,5529],{"class":5428},[5384,7397,7398],{"class":5394},"\"Linspace: ",[5384,7400,5535],{"class":5428},[5384,7402,7403],{"class":5509},"linspace",[5384,7405,5545],{"class":5428},[5384,7407,5548],{"class":5394},[5384,7409,5551],{"class":5509},[5615,7411,7412],{"v-slot:output":5379},[5618,7413,7414,7430],{},[5621,7415,7416],{},[5624,7417,7418,7421,7424,7427],{},[5627,7419,7420],{"align":5629},"Метод генерації",[5627,7422,7423],{"align":5629},"Згенерований вектор",[5627,7425,7426],{"align":5629},"Параметри виклику",[5627,7428,7429],{"align":5629},"Призначення",[5639,7431,7432,7458,7479],{},[5624,7433,7434,7439,7444,7455],{},[5644,7435,7436],{"align":5629},[5381,7437,7438],{},"torch.arange(0, 10, 2)",[5644,7440,7441],{"align":5629},[5381,7442,7443],{},"tensor([0, 2, 4, 6, 8])",[5644,7445,7446,7447,7449,7450,7452,7453],{"align":5629},"Старт ",[5381,7448,5605],{},", стоп ",[5381,7451,7325],{},", крок ",[5381,7454,6022],{},[5644,7456,7457],{"align":5629},"Індексація та цикли",[5624,7459,7460,7465,7470,7476],{},[5644,7461,7462],{"align":5629},[5381,7463,7464],{},"torch.linspace(0, 1, 5)",[5644,7466,7467],{"align":5629},[5381,7468,7469],{},"tensor([0.00, 0.25, 0.50, 0.75, 1.00])",[5644,7471,7472,7473,6523],{"align":5629},"$N=5$ рівномірних точок на $",[5384,7474,7475],{},"0, 1",[5644,7477,7478],{"align":5629},"Сітки значень та графіки",[5624,7480,7481,7486,7491,7494],{},[5644,7482,7483],{"align":5629},[5381,7484,7485],{},"torch.logspace(0, 3, 4)",[5644,7487,7488],{"align":5629},[5381,7489,7490],{},"tensor([1., 10., 100., 1000.])",[5644,7492,7493],{"align":5629},"Степені $10^0 \\dots 10^3$ ($N=4$)",[5644,7495,7496],{"align":5629},"Сітки гіперпараметрів (Learning Rate)",[5979,7498,7500],{"id":7499},"_4-випадкові-тензори","4. Випадкові тензори",[5262,7502,7503],{},"Ініціалізація випадковими значеннями — критично важлива для навчання нейронних мереж. PyTorch пропонує кілька розподілів:",[5490,7505,7507],{"title":7506},"random_tensors.ipynb",[5987,7508,7509,7688],{"execution-count":5493},[5374,7510,7512],{"className":5496,"code":7511,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Випадкові числа з рівномірного розподілу [0, 1)\nuniform = torch.rand(2, 3)\nprint(f\"Рівномірний розподіл [0, 1):\\n{uniform}\\n\")\n\n# Випадкові числа з нормального розподілу N(0, 1)\nnormal = torch.randn(2, 3)\nprint(f\"Нормальний розподіл N(0, 1):\\n{normal}\\n\")\n\n# Випадкові цілі числа\nrandint = torch.randint(low=0, high=10, size=(3, 4))\nprint(f\"Випадкові цілі [0, 10):\\n{randint}\")\n",[5381,7513,7514,7520,7524,7529,7542,7568,7572,7577,7590,7616,7620,7625,7664],{"__ignoreMap":5379},[5384,7515,7516,7518],{"class":5386,"line":5387},[5384,7517,5506],{"class":5505},[5384,7519,5510],{"class":5509},[5384,7521,7522],{"class":5386,"line":5513},[5384,7523,5517],{"emptyLinePlaceholder":5516},[5384,7525,7526],{"class":5386,"line":5520},[5384,7527,7528],{"class":6008},"# Випадкові числа з рівномірного розподілу [0, 1)\n",[5384,7530,7531,7534,7536,7538,7540],{"class":5386,"line":5554},[5384,7532,7533],{"class":5509},"uniform = torch.rand(",[5384,7535,6022],{"class":5604},[5384,7537,6019],{"class":5509},[5384,7539,5743],{"class":5604},[5384,7541,5551],{"class":5509},[5384,7543,7544,7546,7548,7550,7553,7555,7557,7560,7562,7564,7566],{"class":5386,"line":5577},[5384,7545,5523],{"class":5390},[5384,7547,5526],{"class":5509},[5384,7549,5529],{"class":5428},[5384,7551,7552],{"class":5394},"\"Рівномірний розподіл [0, 1):",[5384,7554,6238],{"class":6237},[5384,7556,5535],{"class":5428},[5384,7558,7559],{"class":5509},"uniform",[5384,7561,5545],{"class":5428},[5384,7563,6238],{"class":6237},[5384,7565,5548],{"class":5394},[5384,7567,5551],{"class":5509},[5384,7569,7570],{"class":5386,"line":5586},[5384,7571,5517],{"emptyLinePlaceholder":5516},[5384,7573,7574],{"class":5386,"line":6086},[5384,7575,7576],{"class":6008},"# Випадкові числа з нормального розподілу N(0, 1)\n",[5384,7578,7579,7582,7584,7586,7588],{"class":5386,"line":6273},[5384,7580,7581],{"class":5509},"normal = torch.randn(",[5384,7583,6022],{"class":5604},[5384,7585,6019],{"class":5509},[5384,7587,5743],{"class":5604},[5384,7589,5551],{"class":5509},[5384,7591,7592,7594,7596,7598,7601,7603,7605,7608,7610,7612,7614],{"class":5386,"line":6713},[5384,7593,5523],{"class":5390},[5384,7595,5526],{"class":5509},[5384,7597,5529],{"class":5428},[5384,7599,7600],{"class":5394},"\"Нормальний розподіл N(0, 1):",[5384,7602,6238],{"class":6237},[5384,7604,5535],{"class":5428},[5384,7606,7607],{"class":5509},"normal",[5384,7609,5545],{"class":5428},[5384,7611,6238],{"class":6237},[5384,7613,5548],{"class":5394},[5384,7615,5551],{"class":5509},[5384,7617,7618],{"class":5386,"line":6737},[5384,7619,5517],{"emptyLinePlaceholder":5516},[5384,7621,7622],{"class":5386,"line":6760},[5384,7623,7624],{"class":6008},"# Випадкові цілі числа\n",[5384,7626,7627,7630,7633,7636,7638,7640,7643,7645,7647,7649,7652,7655,7657,7659,7661],{"class":5386,"line":7162},[5384,7628,7629],{"class":5509},"randint = torch.randint(",[5384,7631,7632],{"class":5541},"low",[5384,7634,7635],{"class":5509},"=",[5384,7637,5605],{"class":5604},[5384,7639,6019],{"class":5509},[5384,7641,7642],{"class":5541},"high",[5384,7644,7635],{"class":5509},[5384,7646,7325],{"class":5604},[5384,7648,6019],{"class":5509},[5384,7650,7651],{"class":5541},"size",[5384,7653,7654],{"class":5509},"=(",[5384,7656,5743],{"class":5604},[5384,7658,6019],{"class":5509},[5384,7660,6031],{"class":5604},[5384,7662,7663],{"class":5509},"))\n",[5384,7665,7666,7668,7670,7672,7675,7677,7679,7682,7684,7686],{"class":5386,"line":7172},[5384,7667,5523],{"class":5390},[5384,7669,5526],{"class":5509},[5384,7671,5529],{"class":5428},[5384,7673,7674],{"class":5394},"\"Випадкові цілі [0, 10):",[5384,7676,6238],{"class":6237},[5384,7678,5535],{"class":5428},[5384,7680,7681],{"class":5509},"randint",[5384,7683,5545],{"class":5428},[5384,7685,5548],{"class":5394},[5384,7687,5551],{"class":5509},[5615,7689,7690],{"v-slot:output":5379},[5618,7691,7692,7707],{},[5621,7693,7694],{},[5624,7695,7696,7699,7701,7704],{},[5627,7697,7698],{"align":5629},"Генератор розподілу",[5627,7700,6526],{"align":5636},[5627,7702,7703],{"align":5636},"Діапазон значень",[5627,7705,7706],{"align":5629},"Застосування в Deep Learning",[5639,7708,7709,7726,7743,7760],{},[5624,7710,7711,7716,7720,7723],{},[5644,7712,7713],{"align":5629},[5381,7714,7715],{},"torch.rand(2, 3)",[5644,7717,7718],{"align":5636},[5381,7719,6341],{},[5644,7721,7722],{"align":5636},"Рівномірний $[0, 1)$",[5644,7724,7725],{"align":5629},"Випадкові ймовірності, маски",[5624,7727,7728,7733,7737,7740],{},[5644,7729,7730],{"align":5629},[5381,7731,7732],{},"torch.randn(2, 3)",[5644,7734,7735],{"align":5636},[5381,7736,6341],{},[5644,7738,7739],{"align":5636},"Нормальний $\\mathcal{N}(0, 1)$",[5644,7741,7742],{"align":5629},"Ініціалізація ваг шарів",[5624,7744,7745,7750,7754,7757],{},[5644,7746,7747],{"align":5629},[5381,7748,7749],{},"torch.randint(0, 10, (2, 3))",[5644,7751,7752],{"align":5636},[5381,7753,6341],{},[5644,7755,7756],{"align":5636},"Цілі числа $[0, 10)$",[5644,7758,7759],{"align":5629},"Синтетичні мітки класів",[5624,7761,7762,7767,7771,7774],{},[5644,7763,7764],{"align":5629},[5381,7765,7766],{},"torch.randperm(5)",[5644,7768,7769],{"align":5636},[5381,7770,6157],{},[5644,7772,7773],{"align":5636},"Перестановка ${0, 1, 2, 3, 4}$",[5644,7775,7776],{"align":5629},"Перемішування індексів батчів",[7010,7778,7779,7782,7783,7786,7787,7031],{},[5220,7780,7781],{},"Відтворюваність експериментів:"," За замовчуванням випадкові числа генеруються з різним зерном (",[5224,7784,7785],{},"seed",") при кожному запуску, що ускладнює відтворення результатів. Для детермінованих експериментів використовуйте ",[5381,7788,7789],{},"torch.manual_seed()",[5490,7791,7793,7893],{"title":7792},"reproducibility.ipynb",[5987,7794,7795,7846],{"execution-count":5493},[5374,7796,7798],{"className":5496,"code":7797,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Без фіксації зерна — різні результати\nprint(\"Перший запуск:\", torch.randn(3))\nprint(\"Другий запуск:\", torch.randn(3))\n",[5381,7799,7800,7806,7810,7815,7831],{"__ignoreMap":5379},[5384,7801,7802,7804],{"class":5386,"line":5387},[5384,7803,5506],{"class":5505},[5384,7805,5510],{"class":5509},[5384,7807,7808],{"class":5386,"line":5513},[5384,7809,5517],{"emptyLinePlaceholder":5516},[5384,7811,7812],{"class":5386,"line":5520},[5384,7813,7814],{"class":6008},"# Без фіксації зерна — різні результати\n",[5384,7816,7817,7819,7821,7824,7827,7829],{"class":5386,"line":5554},[5384,7818,5523],{"class":5390},[5384,7820,5526],{"class":5509},[5384,7822,7823],{"class":5394},"\"Перший запуск:\"",[5384,7825,7826],{"class":5509},", torch.randn(",[5384,7828,5743],{"class":5604},[5384,7830,7663],{"class":5509},[5384,7832,7833,7835,7837,7840,7842,7844],{"class":5386,"line":5577},[5384,7834,5523],{"class":5390},[5384,7836,5526],{"class":5509},[5384,7838,7839],{"class":5394},"\"Другий запуск:\"",[5384,7841,7826],{"class":5509},[5384,7843,5743],{"class":5604},[5384,7845,7663],{"class":5509},[5615,7847,7848],{"v-slot:output":5379},[5618,7849,7850,7871],{},[5621,7851,7852],{},[5624,7853,7854,7857,7863,7868],{},[5627,7855,7856],{"align":5629},"Сценарій генерації",[5627,7858,7859,7860],{"align":5629},"Перший виклик ",[5381,7861,7862],{},"randn(3)",[5627,7864,7865,7866],{"align":5629},"Другий виклик ",[5381,7867,7862],{},[5627,7869,7870],{"align":5636},"Стан відтворюваності",[5639,7872,7873],{},[5624,7874,7875,7880,7885,7890],{},[5644,7876,7877],{"align":5629},[5220,7878,7879],{},"Без фіксації seed",[5644,7881,7882],{"align":5629},[5381,7883,7884],{},"[ 0.3421, -1.2345,  0.7890]",[5644,7886,7887],{"align":5629},[5381,7888,7889],{},"[-0.5678,  0.9012, -0.3456]",[5644,7891,7892],{"align":5636},"❌ Різні значення (стохастичність)",[5987,7894,7895,7955],{"execution-count":6022},[5374,7896,7898],{"className":5496,"code":7897,"language":5498,"meta":5379,"style":5379},"# З фіксацією зерна — ідентичні результати\ntorch.manual_seed(42)\nprint(\"Перший запуск:\", torch.randn(3))\n\ntorch.manual_seed(42)\nprint(\"Другий запуск:\", torch.randn(3))\n",[5381,7899,7900,7905,7915,7929,7933,7941],{"__ignoreMap":5379},[5384,7901,7902],{"class":5386,"line":5387},[5384,7903,7904],{"class":6008},"# З фіксацією зерна — ідентичні результати\n",[5384,7906,7907,7910,7913],{"class":5386,"line":5513},[5384,7908,7909],{"class":5509},"torch.manual_seed(",[5384,7911,7912],{"class":5604},"42",[5384,7914,5551],{"class":5509},[5384,7916,7917,7919,7921,7923,7925,7927],{"class":5386,"line":5520},[5384,7918,5523],{"class":5390},[5384,7920,5526],{"class":5509},[5384,7922,7823],{"class":5394},[5384,7924,7826],{"class":5509},[5384,7926,5743],{"class":5604},[5384,7928,7663],{"class":5509},[5384,7930,7931],{"class":5386,"line":5554},[5384,7932,5517],{"emptyLinePlaceholder":5516},[5384,7934,7935,7937,7939],{"class":5386,"line":5577},[5384,7936,7909],{"class":5509},[5384,7938,7912],{"class":5604},[5384,7940,5551],{"class":5509},[5384,7942,7943,7945,7947,7949,7951,7953],{"class":5386,"line":5586},[5384,7944,5523],{"class":5390},[5384,7946,5526],{"class":5509},[5384,7948,7839],{"class":5394},[5384,7950,7826],{"class":5509},[5384,7952,5743],{"class":5604},[5384,7954,7663],{"class":5509},[5615,7956,7957],{"v-slot:output":5379},[5618,7958,7959,7978],{},[5621,7960,7961],{},[5624,7962,7963,7965,7971,7976],{},[5627,7964,7856],{"align":5629},[5627,7966,7967,7968,5227],{"align":5629},"Перший запуск (",[5381,7969,7970],{},"seed=42",[5627,7972,7973,7974,5227],{"align":5629},"Другий запуск (після reset ",[5381,7975,7970],{},[5627,7977,7870],{"align":5636},[5639,7979,7980],{},[5624,7981,7982,7990,7995,7999],{},[5644,7983,7984],{"align":5629},[5220,7985,7986,7987],{},"З ",[5381,7988,7989],{},"torch.manual_seed(42)",[5644,7991,7992],{"align":5629},[5381,7993,7994],{},"[0.3367, 0.1288, 0.2345]",[5644,7996,7997],{"align":5629},[5381,7998,7994],{},[5644,8000,8001],{"align":5636},"✅ Ідентичні значення (100% детермінізм)",[5979,8003,8005],{"id":8004},"_5-типи-даних-та-пристрої","5. Типи даних та пристрої",[5262,8007,8008,8009,8012,8013,8015,8016,8012,8019,8022],{},"Кожен тензор має два важливі атрибути: ",[5220,8010,8011],{},"тип даних"," (",[5224,8014,6138],{},") та ",[5220,8017,8018],{},"пристрій",[5224,8020,8021],{},"device","), на якому він зберігається.",[5490,8024,8026,8336],{"title":8025},"dtype_device.ipynb",[5987,8027,8028,8230],{"execution-count":5493},[5374,8029,8031],{"className":5496,"code":8030,"language":5498,"meta":5379,"style":5379},"import torch\n\n# За замовчуванням: float32 на CPU\ndefault_tensor = torch.tensor([1.0, 2.0, 3.0])\nprint(f\"Тип даних: {default_tensor.dtype}\")\nprint(f\"Пристрій: {default_tensor.device}\\n\")\n\n# Явне задання типу даних\nint_tensor = torch.tensor([1, 2, 3], dtype=torch.int32)\nfloat64_tensor = torch.tensor([1.0, 2.0], dtype=torch.float64)\n\nprint(f\"int32: {int_tensor} → dtype={int_tensor.dtype}\")\nprint(f\"float64: {float64_tensor} → dtype={float64_tensor.dtype}\")\n",[5381,8032,8033,8039,8043,8048,8067,8089,8113,8117,8122,8145,8163,8167,8199],{"__ignoreMap":5379},[5384,8034,8035,8037],{"class":5386,"line":5387},[5384,8036,5506],{"class":5505},[5384,8038,5510],{"class":5509},[5384,8040,8041],{"class":5386,"line":5513},[5384,8042,5517],{"emptyLinePlaceholder":5516},[5384,8044,8045],{"class":5386,"line":5520},[5384,8046,8047],{"class":6008},"# За замовчуванням: float32 на CPU\n",[5384,8049,8050,8053,8055,8057,8060,8062,8065],{"class":5386,"line":5554},[5384,8051,8052],{"class":5509},"default_tensor = torch.tensor([",[5384,8054,7252],{"class":5604},[5384,8056,6019],{"class":5509},[5384,8058,8059],{"class":5604},"2.0",[5384,8061,6019],{"class":5509},[5384,8063,8064],{"class":5604},"3.0",[5384,8066,6039],{"class":5509},[5384,8068,8069,8071,8073,8075,8078,8080,8083,8085,8087],{"class":5386,"line":5577},[5384,8070,5523],{"class":5390},[5384,8072,5526],{"class":5509},[5384,8074,5529],{"class":5428},[5384,8076,8077],{"class":5394},"\"Тип даних: ",[5384,8079,5535],{"class":5428},[5384,8081,8082],{"class":5509},"default_tensor.dtype",[5384,8084,5545],{"class":5428},[5384,8086,5548],{"class":5394},[5384,8088,5551],{"class":5509},[5384,8090,8091,8093,8095,8097,8100,8102,8105,8107,8109,8111],{"class":5386,"line":5586},[5384,8092,5523],{"class":5390},[5384,8094,5526],{"class":5509},[5384,8096,5529],{"class":5428},[5384,8098,8099],{"class":5394},"\"Пристрій: ",[5384,8101,5535],{"class":5428},[5384,8103,8104],{"class":5509},"default_tensor.device",[5384,8106,5545],{"class":5428},[5384,8108,6238],{"class":6237},[5384,8110,5548],{"class":5394},[5384,8112,5551],{"class":5509},[5384,8114,8115],{"class":5386,"line":6086},[5384,8116,5517],{"emptyLinePlaceholder":5516},[5384,8118,8119],{"class":5386,"line":6273},[5384,8120,8121],{"class":6008},"# Явне задання типу даних\n",[5384,8123,8124,8127,8129,8131,8133,8135,8137,8140,8142],{"class":5386,"line":6713},[5384,8125,8126],{"class":5509},"int_tensor = torch.tensor([",[5384,8128,5493],{"class":5604},[5384,8130,6019],{"class":5509},[5384,8132,6022],{"class":5604},[5384,8134,6019],{"class":5509},[5384,8136,5743],{"class":5604},[5384,8138,8139],{"class":5509},"], ",[5384,8141,6138],{"class":5541},[5384,8143,8144],{"class":5509},"=torch.int32)\n",[5384,8146,8147,8150,8152,8154,8156,8158,8160],{"class":5386,"line":6737},[5384,8148,8149],{"class":5509},"float64_tensor = torch.tensor([",[5384,8151,7252],{"class":5604},[5384,8153,6019],{"class":5509},[5384,8155,8059],{"class":5604},[5384,8157,8139],{"class":5509},[5384,8159,6138],{"class":5541},[5384,8161,8162],{"class":5509},"=torch.float64)\n",[5384,8164,8165],{"class":5386,"line":6760},[5384,8166,5517],{"emptyLinePlaceholder":5516},[5384,8168,8169,8171,8173,8175,8178,8180,8183,8185,8188,8190,8193,8195,8197],{"class":5386,"line":7162},[5384,8170,5523],{"class":5390},[5384,8172,5526],{"class":5509},[5384,8174,5529],{"class":5428},[5384,8176,8177],{"class":5394},"\"int32: ",[5384,8179,5535],{"class":5428},[5384,8181,8182],{"class":5509},"int_tensor",[5384,8184,5545],{"class":5428},[5384,8186,8187],{"class":5394}," → dtype=",[5384,8189,5535],{"class":5428},[5384,8191,8192],{"class":5509},"int_tensor.dtype",[5384,8194,5545],{"class":5428},[5384,8196,5548],{"class":5394},[5384,8198,5551],{"class":5509},[5384,8200,8201,8203,8205,8207,8210,8212,8215,8217,8219,8221,8224,8226,8228],{"class":5386,"line":7172},[5384,8202,5523],{"class":5390},[5384,8204,5526],{"class":5509},[5384,8206,5529],{"class":5428},[5384,8208,8209],{"class":5394},"\"float64: ",[5384,8211,5535],{"class":5428},[5384,8213,8214],{"class":5509},"float64_tensor",[5384,8216,5545],{"class":5428},[5384,8218,8187],{"class":5394},[5384,8220,5535],{"class":5428},[5384,8222,8223],{"class":5509},"float64_tensor.dtype",[5384,8225,5545],{"class":5428},[5384,8227,5548],{"class":5394},[5384,8229,5551],{"class":5509},[5615,8231,8232],{"v-slot:output":5379},[5618,8233,8234,8252],{},[5621,8235,8236],{},[5624,8237,8238,8241,8243,8247],{},[5627,8239,8240],{"align":5629},"Операція \u002F Тип",[5627,8242,7208],{"align":5629},[5627,8244,6135,8245,5227],{"align":5636},[5381,8246,6138],{},[5627,8248,8249,8250,5227],{"align":5636},"Пристрій (",[5381,8251,8021],{},[5639,8253,8254,8275,8296,8316],{},[5624,8255,8256,8261,8266,8270],{},[5644,8257,8258],{"align":5629},[5220,8259,8260],{},"Базовий тензор",[5644,8262,8263],{"align":5629},[5381,8264,8265],{},"tensor([1., 2., 3.])",[5644,8267,8268],{"align":5636},[5381,8269,7239],{},[5644,8271,8272],{"align":5636},[5381,8273,8274],{},"cpu",[5624,8276,8277,8282,8287,8292],{},[5644,8278,8279],{"align":5629},[5381,8280,8281],{},".to(torch.int32)",[5644,8283,8284],{"align":5629},[5381,8285,8286],{},"tensor([1, 2, 3])",[5644,8288,8289],{"align":5636},[5381,8290,8291],{},"torch.int32",[5644,8293,8294],{"align":5636},[5381,8295,8274],{},[5624,8297,8298,8303,8307,8312],{},[5644,8299,8300],{"align":5629},[5381,8301,8302],{},".to(torch.float64)",[5644,8304,8305],{"align":5629},[5381,8306,8265],{},[5644,8308,8309],{"align":5636},[5381,8310,8311],{},"torch.float64",[5644,8313,8314],{"align":5636},[5381,8315,8274],{},[5624,8317,8318,8323,8327,8332],{},[5644,8319,8320],{"align":5629},[5381,8321,8322],{},".half()",[5644,8324,8325],{"align":5629},[5381,8326,8265],{},[5644,8328,8329],{"align":5636},[5381,8330,8331],{},"torch.float16",[5644,8333,8334],{"align":5636},[5381,8335,8274],{},[5987,8337,8338,8511],{"execution-count":6022},[5374,8339,8341],{"className":5496,"code":8340,"language":5498,"meta":5379,"style":5379},"# Перевірка доступності GPU\nif torch.cuda.is_available():\n    # Створення тензора безпосередньо на GPU\n    gpu_tensor = torch.tensor([1.0, 2.0, 3.0], device='cuda')\n    print(f\"GPU тензор: {gpu_tensor}\")\n    print(f\"Пристрій: {gpu_tensor.device}\")\n    \n    # Переміщення з CPU на GPU\n    cpu_tensor = torch.tensor([4.0, 5.0, 6.0])\n    gpu_tensor = cpu_tensor.to('cuda')\n    print(f\"\\nПісля .to('cuda'): {gpu_tensor.device}\")\nelse:\n    print(\"GPU недоступна, використовуємо CPU\")\n",[5381,8342,8343,8348,8354,8359,8385,8407,8428,8433,8438,8458,8467,8492,8500],{"__ignoreMap":5379},[5384,8344,8345],{"class":5386,"line":5387},[5384,8346,8347],{"class":6008},"# Перевірка доступності GPU\n",[5384,8349,8350,8352],{"class":5386,"line":5513},[5384,8351,5580],{"class":5505},[5384,8353,5583],{"class":5509},[5384,8355,8356],{"class":5386,"line":5520},[5384,8357,8358],{"class":6008},"    # Створення тензора безпосередньо на GPU\n",[5384,8360,8361,8364,8366,8368,8370,8372,8374,8376,8378,8380,8383],{"class":5386,"line":5554},[5384,8362,8363],{"class":5509},"    gpu_tensor = torch.tensor([",[5384,8365,7252],{"class":5604},[5384,8367,6019],{"class":5509},[5384,8369,8059],{"class":5604},[5384,8371,6019],{"class":5509},[5384,8373,8064],{"class":5604},[5384,8375,8139],{"class":5509},[5384,8377,8021],{"class":5541},[5384,8379,7635],{"class":5509},[5384,8381,8382],{"class":5394},"'cuda'",[5384,8384,5551],{"class":5509},[5384,8386,8387,8389,8391,8393,8396,8398,8401,8403,8405],{"class":5386,"line":5577},[5384,8388,5589],{"class":5390},[5384,8390,5526],{"class":5509},[5384,8392,5529],{"class":5428},[5384,8394,8395],{"class":5394},"\"GPU тензор: ",[5384,8397,5535],{"class":5428},[5384,8399,8400],{"class":5509},"gpu_tensor",[5384,8402,5545],{"class":5428},[5384,8404,5548],{"class":5394},[5384,8406,5551],{"class":5509},[5384,8408,8409,8411,8413,8415,8417,8419,8422,8424,8426],{"class":5386,"line":5586},[5384,8410,5589],{"class":5390},[5384,8412,5526],{"class":5509},[5384,8414,5529],{"class":5428},[5384,8416,8099],{"class":5394},[5384,8418,5535],{"class":5428},[5384,8420,8421],{"class":5509},"gpu_tensor.device",[5384,8423,5545],{"class":5428},[5384,8425,5548],{"class":5394},[5384,8427,5551],{"class":5509},[5384,8429,8430],{"class":5386,"line":6086},[5384,8431,8432],{"class":5509},"    \n",[5384,8434,8435],{"class":5386,"line":6273},[5384,8436,8437],{"class":6008},"    # Переміщення з CPU на GPU\n",[5384,8439,8440,8443,8446,8448,8451,8453,8456],{"class":5386,"line":6713},[5384,8441,8442],{"class":5509},"    cpu_tensor = torch.tensor([",[5384,8444,8445],{"class":5604},"4.0",[5384,8447,6019],{"class":5509},[5384,8449,8450],{"class":5604},"5.0",[5384,8452,6019],{"class":5509},[5384,8454,8455],{"class":5604},"6.0",[5384,8457,6039],{"class":5509},[5384,8459,8460,8463,8465],{"class":5386,"line":6737},[5384,8461,8462],{"class":5509},"    gpu_tensor = cpu_tensor.to(",[5384,8464,8382],{"class":5394},[5384,8466,5551],{"class":5509},[5384,8468,8469,8471,8473,8475,8477,8479,8482,8484,8486,8488,8490],{"class":5386,"line":6760},[5384,8470,5589],{"class":5390},[5384,8472,5526],{"class":5509},[5384,8474,5529],{"class":5428},[5384,8476,5548],{"class":5394},[5384,8478,6238],{"class":6237},[5384,8480,8481],{"class":5394},"Після .to('cuda'): ",[5384,8483,5535],{"class":5428},[5384,8485,8421],{"class":5509},[5384,8487,5545],{"class":5428},[5384,8489,5548],{"class":5394},[5384,8491,5551],{"class":5509},[5384,8493,8494,8497],{"class":5386,"line":7162},[5384,8495,8496],{"class":5505},"else",[5384,8498,8499],{"class":5509},":\n",[5384,8501,8502,8504,8506,8509],{"class":5386,"line":7172},[5384,8503,5589],{"class":5390},[5384,8505,5526],{"class":5509},[5384,8507,8508],{"class":5394},"\"GPU недоступна, використовуємо CPU\"",[5384,8510,5551],{"class":5509},[5615,8512,8513],{"v-slot:output":5379},[5618,8514,8515,8533],{},[5621,8516,8517],{},[5624,8518,8519,8522,8527,8530],{},[5627,8520,8521],{"align":5629},"Об'єкт тензора",[5627,8523,8524,8525,5227],{"align":5636},"Апаратний пристрій (",[5381,8526,8021],{},[5627,8528,8529],{"align":5636},"Тип пам'яті",[5627,8531,8532],{"align":5629},"Статус операції",[5639,8534,8535,8552,8570],{},[5624,8536,8537,8542,8546,8549],{},[5644,8538,8539],{"align":5629},[5381,8540,8541],{},"t_cpu",[5644,8543,8544],{"align":5636},[5381,8545,8274],{},[5644,8547,8548],{"align":5636},"ОЗП комп'ютера",[5644,8550,8551],{"align":5629},"Вихідне створення",[5624,8553,8554,8559,8564,8567],{},[5644,8555,8556],{"align":5629},[5381,8557,8558],{},"t_gpu = t_cpu.to('cuda')",[5644,8560,8561],{"align":5636},[5381,8562,8563],{},"cuda:0",[5644,8565,8566],{"align":5636},"VRAM відеокарти",[5644,8568,8569],{"align":5629},"⚡ Переміщено на прискорювач",[5624,8571,8572,8577,8581,8583],{},[5644,8573,8574],{"align":5629},[5381,8575,8576],{},"t_back = t_gpu.to('cpu')",[5644,8578,8579],{"align":5636},[5381,8580,8274],{},[5644,8582,8548],{"align":5636},[5644,8584,8585],{"align":5629},"✅ Повернено для збереження\u002Fвиводу",[5262,8587,8588],{},[5268,8589],{"alt":8590,"className":8591,"src":8592},"Міграція тензорів між системною пам'яттю CPU Host та відеопам'яттю GPU VRAM",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F03.png",[5275,8594,8596,8598,8601,8604,8607],{"alt":8595},"Потік переміщення тензорів між CPU та GPU",[5262,8597,5280],{},[5262,8599,8600],{},"skinparam sequence {\nActorBorderColor #64748B\nLifeLineBorderColor #94A3B8\nParticipantBorderColor #3B82F6\nParticipantBackgroundColor #EFF6FF\nArrowColor #2563EB\n}",[5262,8602,8603],{},"participant \"Хост-пам'ять (CPU RAM)\" as RAM #EFF6FF\nparticipant \"PCIe 4.0 \u002F Unified Bus\" as BUS #FEF3C7\nparticipant \"Відеопам'ять (GPU VRAM)\" as VRAM #ECFDF5",[5262,8605,8606],{},"RAM -> BUS : t_cpu (тензор у системній пам'яті)\nBUS -> VRAM : .to('cuda') або .cuda()\nnote over VRAM : Високошвидкісні обчислення\\nна тензорних ядрах GPU\nVRAM -> BUS : t_gpu.to('cpu')\nBUS -> RAM : Повернення результату в NumPy \u002F Python",[5262,8608,5292],{},[6588,8610,8611,8616],{},[5262,8612,8613],{},[5220,8614,8615],{},"Найпопулярніші типи даних:",[5194,8617,8618,8627,8635,8640],{},[5197,8619,8620,8622,8623,8626],{},[5381,8621,7239],{}," (або ",[5381,8624,8625],{},"torch.float",") — за замовчуванням для нейронних мереж",[5197,8628,8629,8622,8631,8634],{},[5381,8630,6166],{},[5381,8632,8633],{},"torch.long",") — для індексів та міток класів",[5197,8636,8637,8639],{},[5381,8638,8331],{}," — для економії пам'яті на GPU (mixed precision training)",[5197,8641,8642,8645],{},[5381,8643,8644],{},"torch.bool"," — для булевих масок та фільтрації",[5695,8647,8649],{"id":8648},"анатомія-тензора","Анатомія тензора",[5262,8651,8652],{},"Кожен тензор має набір атрибутів, які описують його властивості:",[5490,8654,8656,8867],{"title":8655,"execution-count":5493},"tensor_attributes.ipynb",[5374,8657,8659],{"className":5496,"code":8658,"language":5498,"meta":5379,"style":5379},"import torch\n\ntensor = torch.randn(3, 4, 5)\n\nprint(f\"Форма (shape): {tensor.shape}\")           # розміри по кожній вісі\nprint(f\"Розмір (size): {tensor.size()}\")          # те саме, що shape\nprint(f\"Ранг (ndim): {tensor.ndim}\")             # кількість вимірів\nprint(f\"Кількість елементів: {tensor.numel()}\")  # загальна кількість\nprint(f\"Тип даних (dtype): {tensor.dtype}\")      # тип чисел\nprint(f\"Пристрій (device): {tensor.device}\")     # CPU або GPU\nprint(f\"Відстеження градієнтів: {tensor.requires_grad}\")\n",[5381,8660,8661,8667,8671,8688,8692,8717,8743,8769,8794,8819,8845],{"__ignoreMap":5379},[5384,8662,8663,8665],{"class":5386,"line":5387},[5384,8664,5506],{"class":5505},[5384,8666,5510],{"class":5509},[5384,8668,8669],{"class":5386,"line":5513},[5384,8670,5517],{"emptyLinePlaceholder":5516},[5384,8672,8673,8676,8678,8680,8682,8684,8686],{"class":5386,"line":5520},[5384,8674,8675],{"class":5509},"tensor = torch.randn(",[5384,8677,5743],{"class":5604},[5384,8679,6019],{"class":5509},[5384,8681,6031],{"class":5604},[5384,8683,6019],{"class":5509},[5384,8685,6036],{"class":5604},[5384,8687,5551],{"class":5509},[5384,8689,8690],{"class":5386,"line":5554},[5384,8691,5517],{"emptyLinePlaceholder":5516},[5384,8693,8694,8696,8698,8700,8703,8705,8707,8709,8711,8714],{"class":5386,"line":5577},[5384,8695,5523],{"class":5390},[5384,8697,5526],{"class":5509},[5384,8699,5529],{"class":5428},[5384,8701,8702],{"class":5394},"\"Форма (shape): ",[5384,8704,5535],{"class":5428},[5384,8706,5940],{"class":5509},[5384,8708,5545],{"class":5428},[5384,8710,5548],{"class":5394},[5384,8712,8713],{"class":5509},")           ",[5384,8715,8716],{"class":6008},"# розміри по кожній вісі\n",[5384,8718,8719,8721,8723,8725,8728,8730,8733,8735,8737,8740],{"class":5386,"line":5586},[5384,8720,5523],{"class":5390},[5384,8722,5526],{"class":5509},[5384,8724,5529],{"class":5428},[5384,8726,8727],{"class":5394},"\"Розмір (size): ",[5384,8729,5535],{"class":5428},[5384,8731,8732],{"class":5509},"tensor.size()",[5384,8734,5545],{"class":5428},[5384,8736,5548],{"class":5394},[5384,8738,8739],{"class":5509},")          ",[5384,8741,8742],{"class":6008},"# те саме, що shape\n",[5384,8744,8745,8747,8749,8751,8754,8756,8759,8761,8763,8766],{"class":5386,"line":6086},[5384,8746,5523],{"class":5390},[5384,8748,5526],{"class":5509},[5384,8750,5529],{"class":5428},[5384,8752,8753],{"class":5394},"\"Ранг (ndim): ",[5384,8755,5535],{"class":5428},[5384,8757,8758],{"class":5509},"tensor.ndim",[5384,8760,5545],{"class":5428},[5384,8762,5548],{"class":5394},[5384,8764,8765],{"class":5509},")             ",[5384,8767,8768],{"class":6008},"# кількість вимірів\n",[5384,8770,8771,8773,8775,8777,8780,8782,8785,8787,8789,8791],{"class":5386,"line":6273},[5384,8772,5523],{"class":5390},[5384,8774,5526],{"class":5509},[5384,8776,5529],{"class":5428},[5384,8778,8779],{"class":5394},"\"Кількість елементів: ",[5384,8781,5535],{"class":5428},[5384,8783,8784],{"class":5509},"tensor.numel()",[5384,8786,5545],{"class":5428},[5384,8788,5548],{"class":5394},[5384,8790,6499],{"class":5509},[5384,8792,8793],{"class":6008},"# загальна кількість\n",[5384,8795,8796,8798,8800,8802,8805,8807,8809,8811,8813,8816],{"class":5386,"line":6713},[5384,8797,5523],{"class":5390},[5384,8799,5526],{"class":5509},[5384,8801,5529],{"class":5428},[5384,8803,8804],{"class":5394},"\"Тип даних (dtype): ",[5384,8806,5535],{"class":5428},[5384,8808,6774],{"class":5509},[5384,8810,5545],{"class":5428},[5384,8812,5548],{"class":5394},[5384,8814,8815],{"class":5509},")      ",[5384,8817,8818],{"class":6008},"# тип чисел\n",[5384,8820,8821,8823,8825,8827,8830,8832,8835,8837,8839,8842],{"class":5386,"line":6737},[5384,8822,5523],{"class":5390},[5384,8824,5526],{"class":5509},[5384,8826,5529],{"class":5428},[5384,8828,8829],{"class":5394},"\"Пристрій (device): ",[5384,8831,5535],{"class":5428},[5384,8833,8834],{"class":5509},"tensor.device",[5384,8836,5545],{"class":5428},[5384,8838,5548],{"class":5394},[5384,8840,8841],{"class":5509},")     ",[5384,8843,8844],{"class":6008},"# CPU або GPU\n",[5384,8846,8847,8849,8851,8853,8856,8858,8861,8863,8865],{"class":5386,"line":6760},[5384,8848,5523],{"class":5390},[5384,8850,5526],{"class":5509},[5384,8852,5529],{"class":5428},[5384,8854,8855],{"class":5394},"\"Відстеження градієнтів: ",[5384,8857,5535],{"class":5428},[5384,8859,8860],{"class":5509},"tensor.requires_grad",[5384,8862,5545],{"class":5428},[5384,8864,5548],{"class":5394},[5384,8866,5551],{"class":5509},[5615,8868,8869],{"v-slot:output":5379},[5618,8870,8871,8884],{},[5621,8872,8873],{},[5624,8874,8875,8878,8881],{},[5627,8876,8877],{"align":5629},"Атрибут \u002F Метод",[5627,8879,8880],{"align":5636},"Значення для тензора $(3 \\times 4 \\times 5)$",[5627,8882,8883],{"align":5629},"Семантичний зміст",[5639,8885,8886,8900,8913,8926],{},[5624,8887,8888,8892,8897],{},[5644,8889,8890],{"align":5629},[5381,8891,5940],{},[5644,8893,8894],{"align":5636},[5381,8895,8896],{},"torch.Size([3, 4, 5])",[5644,8898,8899],{"align":5629},"Кортеж вимірів тензора",[5624,8901,8902,8906,8910],{},[5644,8903,8904],{"align":5629},[5381,8905,8732],{},[5644,8907,8908],{"align":5636},[5381,8909,8896],{},[5644,8911,8912],{"align":5629},"Метод для отримання розмірів",[5624,8914,8915,8919,8923],{},[5644,8916,8917],{"align":5629},[5381,8918,8758],{},[5644,8920,8921],{"align":5636},[5381,8922,5743],{},[5644,8924,8925],{"align":5629},"Ранг (кількість осей) тензора",[5624,8927,8928,8932,8937],{},[5644,8929,8930],{"align":5629},[5381,8931,8784],{},[5644,8933,8934],{"align":5636},[5381,8935,8936],{},"60",[5644,8938,8939],{"align":5629},"Загальна кількість скалярів ($3 \\times 4 \\times 5 = 60$)",[5351,8941,8942],{},[5262,8943,8944,8951],{},[5220,8945,8946,8947,8950],{},"Що означає форма ",[5381,8948,8949],{},"(3, 4, 5)","?"," Це тривимірний тензор: перший вимір містить 3 елементи, кожен з яких є матрицею 4×5. Уявіть це як 3 аркуші паперу, на кожному з яких намальована таблиця розміром 4 рядки × 5 стовпців.",[5262,8953,8954],{},[5268,8955],{"alt":8956,"className":8957,"src":8958},"Внутрішня організація пам'яті тензора PyTorch: зв'язок між Storage, Strides та різними представленнями Views",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F04.png",[5275,8960,8962,8964,8967,8988,8999,9009,9012],{"alt":8961},"Внутрішня організація пам'яті: Storage, Shape та Strides",[5262,8963,5280],{},[5262,8965,8966],{},"!define BUF_COLOR E2E8F0\n!define VIEW_COLOR EFF6FF",[5262,8968,8969,8970,8972,8973,8975,8976,8978,8979,8981,8982,8984,8985,8987],{},"rectangle \"Фізичний буфер у пам'яті: Storage (1D масив)\" as storage #BUF_COLOR {\ncard \"",[5384,8971,5605],{}," = 1 | ",[5384,8974,5493],{}," = 2 | ",[5384,8977,6022],{}," = 3 | ",[5384,8980,5743],{}," = 4 | ",[5384,8983,6031],{}," = 5 | ",[5384,8986,6036],{}," = 6\" as raw_mem\n}",[5262,8989,8990,8991,8994,8995,8998],{},"rectangle \"Метадані представлення: View A\" as view_a #VIEW_COLOR {\ncard \"shape = ",[5384,8992,8993],{},"2, 3","\\nstrides = ",[5384,8996,8997],{},"3, 1","\\n(Матриця 2 рядки x 3 стовпці)\" as va_info\n}",[5262,9000,9001,9002,8994,9005,9008],{},"rectangle \"Метадані представлення: View B\" as view_b #VIEW_COLOR {\ncard \"shape = ",[5384,9003,9004],{},"3, 2",[5384,9006,9007],{},"2, 1","\\n(Матриця 3 рядки x 2 стовпці)\" as vb_info\n}",[5262,9010,9011],{},"va_info -down-> raw_mem : спільний буфер (zero-copy)\nvb_info -down-> raw_mem : спільний буфер (zero-copy)",[5262,9013,5292],{},[5979,9015,9017,9018,9021,9022],{"id":9016},"зміна-форми-тензора-view-vs-reshape","Зміна форми тензора: ",[5381,9019,9020],{},"view()"," vs ",[5381,9023,9024],{},"reshape()",[5262,9026,9027],{},"Часто потрібно змінити форму тензора без зміни даних. PyTorch пропонує два методи:",[5490,9029,9031,9265],{"title":9030},"view_reshape.ipynb",[5987,9032,9033,9191],{"execution-count":5493},[5374,9034,9036],{"className":5496,"code":9035,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Початковий тензор 2×6\ntensor = torch.arange(12).reshape(2, 6)\nprint(f\"Оригінал (2×6):\\n{tensor}\\n\")\n\n# Зміна на 3×4 через view\nview_tensor = tensor.view(3, 4)\nprint(f\"view(3, 4):\\n{view_tensor}\\n\")\n\n# Зміна на 4×3 через reshape\nreshape_tensor = tensor.reshape(4, 3)\nprint(f\"reshape(4, 3):\\n{reshape_tensor}\")\n",[5381,9037,9038,9044,9048,9053,9072,9097,9101,9106,9119,9145,9149,9154,9167],{"__ignoreMap":5379},[5384,9039,9040,9042],{"class":5386,"line":5387},[5384,9041,5506],{"class":5505},[5384,9043,5510],{"class":5509},[5384,9045,9046],{"class":5386,"line":5513},[5384,9047,5517],{"emptyLinePlaceholder":5516},[5384,9049,9050],{"class":5386,"line":5520},[5384,9051,9052],{"class":6008},"# Початковий тензор 2×6\n",[5384,9054,9055,9058,9061,9064,9066,9068,9070],{"class":5386,"line":5554},[5384,9056,9057],{"class":5509},"tensor = torch.arange(",[5384,9059,9060],{"class":5604},"12",[5384,9062,9063],{"class":5509},").reshape(",[5384,9065,6022],{"class":5604},[5384,9067,6019],{"class":5509},[5384,9069,6216],{"class":5604},[5384,9071,5551],{"class":5509},[5384,9073,9074,9076,9078,9080,9083,9085,9087,9089,9091,9093,9095],{"class":5386,"line":5577},[5384,9075,5523],{"class":5390},[5384,9077,5526],{"class":5509},[5384,9079,5529],{"class":5428},[5384,9081,9082],{"class":5394},"\"Оригінал (2×6):",[5384,9084,6238],{"class":6237},[5384,9086,5535],{"class":5428},[5384,9088,5226],{"class":5509},[5384,9090,5545],{"class":5428},[5384,9092,6238],{"class":6237},[5384,9094,5548],{"class":5394},[5384,9096,5551],{"class":5509},[5384,9098,9099],{"class":5386,"line":5586},[5384,9100,5517],{"emptyLinePlaceholder":5516},[5384,9102,9103],{"class":5386,"line":6086},[5384,9104,9105],{"class":6008},"# Зміна на 3×4 через view\n",[5384,9107,9108,9111,9113,9115,9117],{"class":5386,"line":6273},[5384,9109,9110],{"class":5509},"view_tensor = tensor.view(",[5384,9112,5743],{"class":5604},[5384,9114,6019],{"class":5509},[5384,9116,6031],{"class":5604},[5384,9118,5551],{"class":5509},[5384,9120,9121,9123,9125,9127,9130,9132,9134,9137,9139,9141,9143],{"class":5386,"line":6713},[5384,9122,5523],{"class":5390},[5384,9124,5526],{"class":5509},[5384,9126,5529],{"class":5428},[5384,9128,9129],{"class":5394},"\"view(3, 4):",[5384,9131,6238],{"class":6237},[5384,9133,5535],{"class":5428},[5384,9135,9136],{"class":5509},"view_tensor",[5384,9138,5545],{"class":5428},[5384,9140,6238],{"class":6237},[5384,9142,5548],{"class":5394},[5384,9144,5551],{"class":5509},[5384,9146,9147],{"class":5386,"line":6737},[5384,9148,5517],{"emptyLinePlaceholder":5516},[5384,9150,9151],{"class":5386,"line":6760},[5384,9152,9153],{"class":6008},"# Зміна на 4×3 через reshape\n",[5384,9155,9156,9159,9161,9163,9165],{"class":5386,"line":7162},[5384,9157,9158],{"class":5509},"reshape_tensor = tensor.reshape(",[5384,9160,6031],{"class":5604},[5384,9162,6019],{"class":5509},[5384,9164,5743],{"class":5604},[5384,9166,5551],{"class":5509},[5384,9168,9169,9171,9173,9175,9178,9180,9182,9185,9187,9189],{"class":5386,"line":7172},[5384,9170,5523],{"class":5390},[5384,9172,5526],{"class":5509},[5384,9174,5529],{"class":5428},[5384,9176,9177],{"class":5394},"\"reshape(4, 3):",[5384,9179,6238],{"class":6237},[5384,9181,5535],{"class":5428},[5384,9183,9184],{"class":5509},"reshape_tensor",[5384,9186,5545],{"class":5428},[5384,9188,5548],{"class":5394},[5384,9190,5551],{"class":5509},[5615,9192,9193],{"v-slot:output":5379},[5618,9194,9195,9211],{},[5621,9196,9197],{},[5624,9198,9199,9202,9205,9208],{},[5627,9200,9201],{"align":5629},"Операція зміни форми",[5627,9203,9204],{"align":5636},"Нова форма",[5627,9206,9207],{"align":5636},"Чи розділяє пам'ять?",[5627,9209,9210],{"align":5629},"Опис операції",[5639,9212,9213,9230,9247],{},[5624,9214,9215,9220,9225,9227],{},[5644,9216,9217],{"align":5629},[5220,9218,9219],{},"Оригінал",[5644,9221,9222],{"align":5636},[5381,9223,9224],{},"torch.Size([2, 6])",[5644,9226,8260],{"align":5636},[5644,9228,9229],{"align":5629},"Послідовні числа від 0 до 11",[5624,9231,9232,9237,9241,9244],{},[5644,9233,9234],{"align":5629},[5381,9235,9236],{},"t.view(3, 4)",[5644,9238,9239],{"align":5636},[5381,9240,7234],{},[5644,9242,9243],{"align":5636},"Так (спільний буфер)",[5644,9245,9246],{"align":5629},"Швидкий перегляд без копіювання",[5624,9248,9249,9254,9259,9262],{},[5644,9250,9251],{"align":5629},[5381,9252,9253],{},"t.reshape(4, 3)",[5644,9255,9256],{"align":5636},[5381,9257,9258],{},"torch.Size([4, 3])",[5644,9260,9261],{"align":5636},"Так \u002F Ні (автоматично)",[5644,9263,9264],{"align":5629},"Безпечна зміна форми будь-якого тензора",[5987,9266,9267,9348],{"execution-count":6022},[5374,9268,9270],{"className":5496,"code":9269,"language":5498,"meta":5379,"style":5379},"# Автоматичне обчислення одного виміру через -1\ntensor = torch.arange(24)\nreshaped = tensor.view(2, -1)  # -1 автоматично стане 12 (бо 24 \u002F 2 = 12)\nprint(f\"view(2, -1):\\n{reshaped}\")\nprint(f\"Форма: {reshaped.shape}\")\n",[5381,9271,9272,9277,9286,9303,9327],{"__ignoreMap":5379},[5384,9273,9274],{"class":5386,"line":5387},[5384,9275,9276],{"class":6008},"# Автоматичне обчислення одного виміру через -1\n",[5384,9278,9279,9281,9284],{"class":5386,"line":5513},[5384,9280,9057],{"class":5509},[5384,9282,9283],{"class":5604},"24",[5384,9285,5551],{"class":5509},[5384,9287,9288,9291,9293,9296,9298,9300],{"class":5386,"line":5520},[5384,9289,9290],{"class":5509},"reshaped = tensor.view(",[5384,9292,6022],{"class":5604},[5384,9294,9295],{"class":5509},", -",[5384,9297,5493],{"class":5604},[5384,9299,6499],{"class":5509},[5384,9301,9302],{"class":6008},"# -1 автоматично стане 12 (бо 24 \u002F 2 = 12)\n",[5384,9304,9305,9307,9309,9311,9314,9316,9318,9321,9323,9325],{"class":5386,"line":5554},[5384,9306,5523],{"class":5390},[5384,9308,5526],{"class":5509},[5384,9310,5529],{"class":5428},[5384,9312,9313],{"class":5394},"\"view(2, -1):",[5384,9315,6238],{"class":6237},[5384,9317,5535],{"class":5428},[5384,9319,9320],{"class":5509},"reshaped",[5384,9322,5545],{"class":5428},[5384,9324,5548],{"class":5394},[5384,9326,5551],{"class":5509},[5384,9328,9329,9331,9333,9335,9337,9339,9342,9344,9346],{"class":5386,"line":5577},[5384,9330,5523],{"class":5390},[5384,9332,5526],{"class":5509},[5384,9334,5529],{"class":5428},[5384,9336,6072],{"class":5394},[5384,9338,5535],{"class":5428},[5384,9340,9341],{"class":5509},"reshaped.shape",[5384,9343,5545],{"class":5428},[5384,9345,5548],{"class":5394},[5384,9347,5551],{"class":5509},[5615,9349,9350],{"v-slot:output":5379},[5618,9351,9352,9371],{},[5621,9353,9354],{},[5624,9355,9356,9362,9365,9368],{},[5627,9357,9358,9359],{"align":5629},"Виклик з параметром ",[5381,9360,9361],{},"-1",[5627,9363,9364],{"align":5636},"Вказана форма",[5627,9366,9367],{"align":5636},"Обчислена форма",[5627,9369,9370],{"align":5629},"Пояснення авто-виміру",[5639,9372,9373,9392,9411],{},[5624,9374,9375,9380,9385,9389],{},[5644,9376,9377],{"align":5629},[5381,9378,9379],{},"t.view(2, -1)",[5644,9381,9382],{"align":5636},[5381,9383,9384],{},"(2, -1)",[5644,9386,9387],{"align":5636},[5381,9388,9224],{},[5644,9390,9391],{"align":5629},"Розмір другого виміру: $12 \u002F 2 = 6$",[5624,9393,9394,9399,9404,9408],{},[5644,9395,9396],{"align":5629},[5381,9397,9398],{},"t.view(-1, 4)",[5644,9400,9401],{"align":5636},[5381,9402,9403],{},"(-1, 4)",[5644,9405,9406],{"align":5636},[5381,9407,7234],{},[5644,9409,9410],{"align":5629},"Розмір першого виміру: $12 \u002F 4 = 3$",[5624,9412,9413,9418,9423,9428],{},[5644,9414,9415],{"align":5629},[5381,9416,9417],{},"t.view(-1)",[5644,9419,9420],{"align":5636},[5381,9421,9422],{},"(-1)",[5644,9424,9425],{"align":5636},[5381,9426,9427],{},"torch.Size([12])",[5644,9429,9430],{"align":5629},"Повне випрямлення (Flatten) у 1D-вектор",[5479,9432,9433,9444,9466],{},[5262,9434,9435],{},[5220,9436,9437,9438,9440,9441,9443],{},"Різниця між ",[5381,9439,9020],{}," та ",[5381,9442,9024],{},":",[5194,9445,9446,9457],{},[5197,9447,9448,9452,9453,9456],{},[5220,9449,9450],{},[5381,9451,9020],{}," вимагає, щоб тензор був ",[5220,9454,9455],{},"contiguous"," у пам'яті (дані йдуть підряд). Якщо це не так — виникне помилка.",[5197,9458,9459,9463,9464,7031],{},[5220,9460,9461],{},[5381,9462,9024],{}," працює завжди: якщо тензор не contiguous, створить копію. Якщо contiguous — працюватиме як ",[5381,9465,9020],{},[5262,9467,9468,9471,9472,9474,9475,9477],{},[5220,9469,9470],{},"Рекомендація:"," Використовуйте ",[5381,9473,9024],{}," для надійності, або ",[5381,9476,9020],{}," якщо впевнені, що тензор contiguous (наприклад, щойно створений).",[5979,9479,9481,9482,9021,9485],{"id":9480},"копіювання-тензорів-clone-vs-detach","Копіювання тензорів: ",[5381,9483,9484],{},"clone()",[5381,9486,9487],{},"detach()",[5262,9489,9490],{},"При роботі з тензорами часто виникає потреба створити копію. PyTorch пропонує два методи з різною семантикою:",[5490,9492,9494],{"title":9493},"clone_detach.ipynb",[5987,9495,9496,9752],{"execution-count":5493},[5374,9497,9499],{"className":5496,"code":9498,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Створимо тензор з відстеженням градієнтів\noriginal = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)\nprint(f\"Оригінал: {original}\")\nprint(f\"requires_grad: {original.requires_grad}\\n\")\n\n# clone() — повна копія з графом обчислень\ncloned = original.clone()\nprint(f\"Після clone(): {cloned}\")\nprint(f\"requires_grad: {cloned.requires_grad}\")\nprint(f\"Ділять пам'ять? {cloned.data_ptr() == original.data_ptr()}\\n\")\n\n# detach() — копія БЕЗ графа обчислень\ndetached = original.detach()\nprint(f\"Після detach(): {detached}\")\nprint(f\"requires_grad: {detached.requires_grad}\")\nprint(f\"Ділять пам'ять? {detached.data_ptr() == original.data_ptr()}\")\n",[5381,9500,9501,9507,9511,9516,9542,9564,9588,9592,9597,9602,9624,9645,9669,9673,9679,9685,9708,9730],{"__ignoreMap":5379},[5384,9502,9503,9505],{"class":5386,"line":5387},[5384,9504,5506],{"class":5505},[5384,9506,5510],{"class":5509},[5384,9508,9509],{"class":5386,"line":5513},[5384,9510,5517],{"emptyLinePlaceholder":5516},[5384,9512,9513],{"class":5386,"line":5520},[5384,9514,9515],{"class":6008},"# Створимо тензор з відстеженням градієнтів\n",[5384,9517,9518,9521,9523,9525,9527,9529,9531,9533,9536,9538,9540],{"class":5386,"line":5554},[5384,9519,9520],{"class":5509},"original = torch.tensor([",[5384,9522,7252],{"class":5604},[5384,9524,6019],{"class":5509},[5384,9526,8059],{"class":5604},[5384,9528,6019],{"class":5509},[5384,9530,8064],{"class":5604},[5384,9532,8139],{"class":5509},[5384,9534,9535],{"class":5541},"requires_grad",[5384,9537,7635],{"class":5509},[5384,9539,5668],{"class":5428},[5384,9541,5551],{"class":5509},[5384,9543,9544,9546,9548,9550,9553,9555,9558,9560,9562],{"class":5386,"line":5577},[5384,9545,5523],{"class":5390},[5384,9547,5526],{"class":5509},[5384,9549,5529],{"class":5428},[5384,9551,9552],{"class":5394},"\"Оригінал: ",[5384,9554,5535],{"class":5428},[5384,9556,9557],{"class":5509},"original",[5384,9559,5545],{"class":5428},[5384,9561,5548],{"class":5394},[5384,9563,5551],{"class":5509},[5384,9565,9566,9568,9570,9572,9575,9577,9580,9582,9584,9586],{"class":5386,"line":5586},[5384,9567,5523],{"class":5390},[5384,9569,5526],{"class":5509},[5384,9571,5529],{"class":5428},[5384,9573,9574],{"class":5394},"\"requires_grad: ",[5384,9576,5535],{"class":5428},[5384,9578,9579],{"class":5509},"original.requires_grad",[5384,9581,5545],{"class":5428},[5384,9583,6238],{"class":6237},[5384,9585,5548],{"class":5394},[5384,9587,5551],{"class":5509},[5384,9589,9590],{"class":5386,"line":6086},[5384,9591,5517],{"emptyLinePlaceholder":5516},[5384,9593,9594],{"class":5386,"line":6273},[5384,9595,9596],{"class":6008},"# clone() — повна копія з графом обчислень\n",[5384,9598,9599],{"class":5386,"line":6713},[5384,9600,9601],{"class":5509},"cloned = original.clone()\n",[5384,9603,9604,9606,9608,9610,9613,9615,9618,9620,9622],{"class":5386,"line":6737},[5384,9605,5523],{"class":5390},[5384,9607,5526],{"class":5509},[5384,9609,5529],{"class":5428},[5384,9611,9612],{"class":5394},"\"Після clone(): ",[5384,9614,5535],{"class":5428},[5384,9616,9617],{"class":5509},"cloned",[5384,9619,5545],{"class":5428},[5384,9621,5548],{"class":5394},[5384,9623,5551],{"class":5509},[5384,9625,9626,9628,9630,9632,9634,9636,9639,9641,9643],{"class":5386,"line":6760},[5384,9627,5523],{"class":5390},[5384,9629,5526],{"class":5509},[5384,9631,5529],{"class":5428},[5384,9633,9574],{"class":5394},[5384,9635,5535],{"class":5428},[5384,9637,9638],{"class":5509},"cloned.requires_grad",[5384,9640,5545],{"class":5428},[5384,9642,5548],{"class":5394},[5384,9644,5551],{"class":5509},[5384,9646,9647,9649,9651,9653,9656,9658,9661,9663,9665,9667],{"class":5386,"line":7162},[5384,9648,5523],{"class":5390},[5384,9650,5526],{"class":5509},[5384,9652,5529],{"class":5428},[5384,9654,9655],{"class":5394},"\"Ділять пам'ять? ",[5384,9657,5535],{"class":5428},[5384,9659,9660],{"class":5509},"cloned.data_ptr() == original.data_ptr()",[5384,9662,5545],{"class":5428},[5384,9664,6238],{"class":6237},[5384,9666,5548],{"class":5394},[5384,9668,5551],{"class":5509},[5384,9670,9671],{"class":5386,"line":7172},[5384,9672,5517],{"emptyLinePlaceholder":5516},[5384,9674,9676],{"class":5386,"line":9675},14,[5384,9677,9678],{"class":6008},"# detach() — копія БЕЗ графа обчислень\n",[5384,9680,9682],{"class":5386,"line":9681},15,[5384,9683,9684],{"class":5509},"detached = original.detach()\n",[5384,9686,9688,9690,9692,9694,9697,9699,9702,9704,9706],{"class":5386,"line":9687},16,[5384,9689,5523],{"class":5390},[5384,9691,5526],{"class":5509},[5384,9693,5529],{"class":5428},[5384,9695,9696],{"class":5394},"\"Після detach(): ",[5384,9698,5535],{"class":5428},[5384,9700,9701],{"class":5509},"detached",[5384,9703,5545],{"class":5428},[5384,9705,5548],{"class":5394},[5384,9707,5551],{"class":5509},[5384,9709,9711,9713,9715,9717,9719,9721,9724,9726,9728],{"class":5386,"line":9710},17,[5384,9712,5523],{"class":5390},[5384,9714,5526],{"class":5509},[5384,9716,5529],{"class":5428},[5384,9718,9574],{"class":5394},[5384,9720,5535],{"class":5428},[5384,9722,9723],{"class":5509},"detached.requires_grad",[5384,9725,5545],{"class":5428},[5384,9727,5548],{"class":5394},[5384,9729,5551],{"class":5509},[5384,9731,9733,9735,9737,9739,9741,9743,9746,9748,9750],{"class":5386,"line":9732},18,[5384,9734,5523],{"class":5390},[5384,9736,5526],{"class":5509},[5384,9738,5529],{"class":5428},[5384,9740,9655],{"class":5394},[5384,9742,5535],{"class":5428},[5384,9744,9745],{"class":5509},"detached.data_ptr() == original.data_ptr()",[5384,9747,5545],{"class":5428},[5384,9749,5548],{"class":5394},[5384,9751,5551],{"class":5509},[5615,9753,9754],{"v-slot:output":5379},[5618,9755,9756,9776],{},[5621,9757,9758],{},[5624,9759,9760,9763,9766,9770,9773],{},[5627,9761,9762],{"align":5629},"Метод копіювання",[5627,9764,9765],{"align":5629},"Форма та значення",[5627,9767,9768],{"align":5636},[5381,9769,9535],{},[5627,9771,9772],{"align":5636},"Чи ділять пам'ять?",[5627,9774,9775],{"align":5629},"Вплив на граф Autograd",[5639,9777,9778,9801],{},[5624,9779,9780,9787,9791,9795,9798],{},[5644,9781,9782],{"align":5629},[5220,9783,9784],{},[5381,9785,9786],{},"t.clone()",[5644,9788,9789],{"align":5629},[5381,9790,8265],{},[5644,9792,9793],{"align":5636},[5381,9794,5668],{},[5644,9796,9797],{"align":5636},"❌ Ні (нова копія в RAM)",[5644,9799,9800],{"align":5629},"Градієнти протікають крізь копію",[5624,9802,9803,9810,9814,9819,9822],{},[5644,9804,9805],{"align":5629},[5220,9806,9807],{},[5381,9808,9809],{},"t.detach()",[5644,9811,9812],{"align":5629},[5381,9813,8265],{},[5644,9815,9816],{"align":5636},[5381,9817,9818],{},"False",[5644,9820,9821],{"align":5636},"✅ Так (спільний буфер)",[5644,9823,9824],{"align":5629},"Від'єднано від графа обчислень",[5186,9826,9827,9854],{},[5189,9828,9831],{"icon":9829,"title":9830},"i-lucide-copy","🔄 .clone()",[5194,9832,9833,9840,9846,9851],{},[5197,9834,9835,9836,9839],{},"Створює ",[5220,9837,9838],{},"нову копію"," даних у пам'яті",[5197,9841,9842,9845],{},[5220,9843,9844],{},"Зберігає"," зв'язок з графом обчислень",[5197,9847,9848,9849],{},"Градієнти розповсюджуються через ",[5381,9850,9484],{},[5197,9852,9853],{},"Використовується, коли потрібна незалежна копія з autograd",[5189,9855,9858],{"icon":9856,"title":9857},"i-lucide-scissors","✂️ .detach()",[5194,9859,9860,9866,9872,9877],{},[5197,9861,9862,9865],{},[5220,9863,9864],{},"Ділить пам'ять"," з оригінальним тензором",[5197,9867,9868,9871],{},[5220,9869,9870],{},"Відключає"," від графа обчислень",[5197,9873,9874,9875],{},"Градієнти НЕ розповсюджуються через ",[5381,9876,9487],{},[5197,9878,9879],{},"Використовується для inference або коли градієнти не потрібні",[5979,9881,9883],{"id":9882},"in-place-операції","In-place операції",[5262,9885,9886,9887,8012,9890,9893,9894,9443],{},"PyTorch підтримує операції, які змінюють тензор ",[5220,9888,9889],{},"на місці",[5224,9891,9892],{},"in-place","), без створення нової копії. Такі методи завжди закінчуються символом підкреслення ",[5381,9895,9896],{},"_",[5490,9898,9900],{"title":9899},"inplace_operations.ipynb",[5987,9901,9902,10130],{"execution-count":5493},[5374,9903,9905],{"className":5496,"code":9904,"language":5498,"meta":5379,"style":5379},"import torch\n\ntensor = torch.tensor([1.0, 2.0, 3.0])\nprint(f\"Оригінал: {tensor}\")\nprint(f\"ID об'єкта: {id(tensor)}\\n\")\n\n# Звичайна операція — створює новий тензор\nresult = tensor.add(10)\nprint(f\"Після add(10): {result}\")\nprint(f\"ID нового тензора: {id(result)}\")\nprint(f\"Оригінал не змінився: {tensor}\\n\")\n\n# In-place операція — змінює існуючий тензор\ntensor.add_(10)\nprint(f\"Після add_(10): {tensor}\")\nprint(f\"ID залишився той самий: {id(tensor)}\")\n",[5381,9906,9907,9913,9917,9934,9954,9981,9985,9990,9999,10021,10045,10068,10072,10077,10086,10107],{"__ignoreMap":5379},[5384,9908,9909,9911],{"class":5386,"line":5387},[5384,9910,5506],{"class":5505},[5384,9912,5510],{"class":5509},[5384,9914,9915],{"class":5386,"line":5513},[5384,9916,5517],{"emptyLinePlaceholder":5516},[5384,9918,9919,9922,9924,9926,9928,9930,9932],{"class":5386,"line":5520},[5384,9920,9921],{"class":5509},"tensor = torch.tensor([",[5384,9923,7252],{"class":5604},[5384,9925,6019],{"class":5509},[5384,9927,8059],{"class":5604},[5384,9929,6019],{"class":5509},[5384,9931,8064],{"class":5604},[5384,9933,6039],{"class":5509},[5384,9935,9936,9938,9940,9942,9944,9946,9948,9950,9952],{"class":5386,"line":5554},[5384,9937,5523],{"class":5390},[5384,9939,5526],{"class":5509},[5384,9941,5529],{"class":5428},[5384,9943,9552],{"class":5394},[5384,9945,5535],{"class":5428},[5384,9947,5226],{"class":5509},[5384,9949,5545],{"class":5428},[5384,9951,5548],{"class":5394},[5384,9953,5551],{"class":5509},[5384,9955,9956,9958,9960,9962,9965,9967,9970,9973,9975,9977,9979],{"class":5386,"line":5577},[5384,9957,5523],{"class":5390},[5384,9959,5526],{"class":5509},[5384,9961,5529],{"class":5428},[5384,9963,9964],{"class":5394},"\"ID об'єкта: ",[5384,9966,5535],{"class":5428},[5384,9968,9969],{"class":5390},"id",[5384,9971,9972],{"class":5509},"(tensor)",[5384,9974,5545],{"class":5428},[5384,9976,6238],{"class":6237},[5384,9978,5548],{"class":5394},[5384,9980,5551],{"class":5509},[5384,9982,9983],{"class":5386,"line":5586},[5384,9984,5517],{"emptyLinePlaceholder":5516},[5384,9986,9987],{"class":5386,"line":6086},[5384,9988,9989],{"class":6008},"# Звичайна операція — створює новий тензор\n",[5384,9991,9992,9995,9997],{"class":5386,"line":6273},[5384,9993,9994],{"class":5509},"result = tensor.add(",[5384,9996,7325],{"class":5604},[5384,9998,5551],{"class":5509},[5384,10000,10001,10003,10005,10007,10010,10012,10015,10017,10019],{"class":5386,"line":6713},[5384,10002,5523],{"class":5390},[5384,10004,5526],{"class":5509},[5384,10006,5529],{"class":5428},[5384,10008,10009],{"class":5394},"\"Після add(10): ",[5384,10011,5535],{"class":5428},[5384,10013,10014],{"class":5509},"result",[5384,10016,5545],{"class":5428},[5384,10018,5548],{"class":5394},[5384,10020,5551],{"class":5509},[5384,10022,10023,10025,10027,10029,10032,10034,10036,10039,10041,10043],{"class":5386,"line":6737},[5384,10024,5523],{"class":5390},[5384,10026,5526],{"class":5509},[5384,10028,5529],{"class":5428},[5384,10030,10031],{"class":5394},"\"ID нового тензора: ",[5384,10033,5535],{"class":5428},[5384,10035,9969],{"class":5390},[5384,10037,10038],{"class":5509},"(result)",[5384,10040,5545],{"class":5428},[5384,10042,5548],{"class":5394},[5384,10044,5551],{"class":5509},[5384,10046,10047,10049,10051,10053,10056,10058,10060,10062,10064,10066],{"class":5386,"line":6760},[5384,10048,5523],{"class":5390},[5384,10050,5526],{"class":5509},[5384,10052,5529],{"class":5428},[5384,10054,10055],{"class":5394},"\"Оригінал не змінився: ",[5384,10057,5535],{"class":5428},[5384,10059,5226],{"class":5509},[5384,10061,5545],{"class":5428},[5384,10063,6238],{"class":6237},[5384,10065,5548],{"class":5394},[5384,10067,5551],{"class":5509},[5384,10069,10070],{"class":5386,"line":7162},[5384,10071,5517],{"emptyLinePlaceholder":5516},[5384,10073,10074],{"class":5386,"line":7172},[5384,10075,10076],{"class":6008},"# In-place операція — змінює існуючий тензор\n",[5384,10078,10079,10082,10084],{"class":5386,"line":9675},[5384,10080,10081],{"class":5509},"tensor.add_(",[5384,10083,7325],{"class":5604},[5384,10085,5551],{"class":5509},[5384,10087,10088,10090,10092,10094,10097,10099,10101,10103,10105],{"class":5386,"line":9681},[5384,10089,5523],{"class":5390},[5384,10091,5526],{"class":5509},[5384,10093,5529],{"class":5428},[5384,10095,10096],{"class":5394},"\"Після add_(10): ",[5384,10098,5535],{"class":5428},[5384,10100,5226],{"class":5509},[5384,10102,5545],{"class":5428},[5384,10104,5548],{"class":5394},[5384,10106,5551],{"class":5509},[5384,10108,10109,10111,10113,10115,10118,10120,10122,10124,10126,10128],{"class":5386,"line":9687},[5384,10110,5523],{"class":5390},[5384,10112,5526],{"class":5509},[5384,10114,5529],{"class":5428},[5384,10116,10117],{"class":5394},"\"ID залишився той самий: ",[5384,10119,5535],{"class":5428},[5384,10121,9969],{"class":5390},[5384,10123,9972],{"class":5509},[5384,10125,5545],{"class":5428},[5384,10127,5548],{"class":5394},[5384,10129,5551],{"class":5509},[5615,10131,10132],{"v-slot:output":5379},[5618,10133,10134,10153],{},[5621,10135,10136],{},[5624,10137,10138,10141,10144,10147,10150],{},[5627,10139,10140],{"align":5629},"Тип операції",[5627,10142,10143],{"align":5629},"Виклик",[5627,10145,10146],{"align":5629},"Результат",[5627,10148,10149],{"align":5636},"ID об'єкта",[5627,10151,10152],{"align":5629},"Вплив на вихідний тензор",[5639,10154,10155,10184],{},[5624,10156,10157,10162,10167,10172,10178],{},[5644,10158,10159],{"align":5629},[5220,10160,10161],{},"Out-of-place",[5644,10163,10164],{"align":5629},[5381,10165,10166],{},"t.add(10)",[5644,10168,10169],{"align":5629},[5381,10170,10171],{},"tensor([11., 12., 13.])",[5644,10173,10174,10177],{"align":5636},[5381,10175,10176],{},"140234567895678"," (новий)",[5644,10179,10180,10181],{"align":5629},"Оригінал залишається ",[5381,10182,10183],{},"[1., 2., 3.]",[5624,10185,10186,10191,10196,10200,10206],{},[5644,10187,10188],{"align":5629},[5220,10189,10190],{},"In-place",[5644,10192,10193],{"align":5629},[5381,10194,10195],{},"t.add_(10)",[5644,10197,10198],{"align":5629},[5381,10199,10171],{},[5644,10201,10202,10205],{"align":5636},[5381,10203,10204],{},"140234567891234"," (той самий)",[5644,10207,10208],{"align":5629},"⚠️ Змінює значення безпосередньо в пам'яті",[7010,10210,10211,10216,10231],{},[5262,10212,10213],{},[5220,10214,10215],{},"Обережно з in-place операціями!",[10217,10218,10219,10225],"ol",{},[5197,10220,10221,10224],{},[5220,10222,10223],{},"Можуть порушити autograd:"," якщо тензор використовується для обчислення градієнтів, in-place зміна може призвести до помилки.",[5197,10226,10227,10230],{},[5220,10228,10229],{},"Ускладнюють налагодження:"," важче відстежити, де саме змінилося значення.",[5262,10232,10233,10235],{},[5220,10234,9470],{}," Використовуйте in-place операції лише для оптимізації пам'яті у критичних місцях (наприклад, при inference на великих моделях).",[5695,10237,10239],{"id":10238},"операції-з-тензорами","Операції з тензорами",[5262,10241,10242],{},"Розглянемо основні операції, які можна виконувати над тензорами.",[5979,10244,10246],{"id":10245},"індексація-та-slicing","Індексація та slicing",[5262,10248,10249],{},"Індексація в PyTorch працює точно так само, як у NumPy:",[5490,10251,10253],{"title":10252},"indexing.ipynb",[5987,10254,10255,10538],{"execution-count":5493},[5374,10256,10258],{"className":5496,"code":10257,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Двовимірний тензор (матриця)\nmatrix = torch.tensor([\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n    [9, 10, 11, 12]\n])\nprint(f\"Матриця:\\n{matrix}\\n\")\n\n# Доступ до одного елемента\nprint(f\"Елемент [1, 2]: {matrix[1, 2]}\")      # рядок 1, стовпець 2 → 7\n\n# Доступ до рядка\nprint(f\"Другий рядок: {matrix[1]}\")           # [5, 6, 7, 8]\n\n# Доступ до стовпця\nprint(f\"Третій стовпець: {matrix[:, 2]}\")     # [3, 7, 11]\n\n# Зріз (slicing)\nprint(f\"Підматриця [0:2, 1:3]:\\n{matrix[0:2, 1:3]}\")\n",[5381,10259,10260,10266,10270,10274,10278,10298,10319,10341,10345,10369,10373,10378,10412,10416,10421,10449,10453,10458,10487,10492,10498],{"__ignoreMap":5379},[5384,10261,10262,10264],{"class":5386,"line":5387},[5384,10263,5506],{"class":5505},[5384,10265,5510],{"class":5509},[5384,10267,10268],{"class":5386,"line":5513},[5384,10269,5517],{"emptyLinePlaceholder":5516},[5384,10271,10272],{"class":5386,"line":5520},[5384,10273,6178],{"class":6008},[5384,10275,10276],{"class":5386,"line":5554},[5384,10277,6183],{"class":5509},[5384,10279,10280,10282,10284,10286,10288,10290,10292,10294,10296],{"class":5386,"line":5577},[5384,10281,6188],{"class":5509},[5384,10283,5493],{"class":5604},[5384,10285,6019],{"class":5509},[5384,10287,6022],{"class":5604},[5384,10289,6019],{"class":5509},[5384,10291,5743],{"class":5604},[5384,10293,6019],{"class":5509},[5384,10295,6031],{"class":5604},[5384,10297,6201],{"class":5509},[5384,10299,10300,10302,10304,10306,10308,10310,10312,10314,10317],{"class":5386,"line":5586},[5384,10301,6188],{"class":5509},[5384,10303,6036],{"class":5604},[5384,10305,6019],{"class":5509},[5384,10307,6216],{"class":5604},[5384,10309,6019],{"class":5509},[5384,10311,7292],{"class":5604},[5384,10313,6019],{"class":5509},[5384,10315,10316],{"class":5604},"8",[5384,10318,6201],{"class":5509},[5384,10320,10321,10323,10326,10328,10330,10332,10335,10337,10339],{"class":5386,"line":6086},[5384,10322,6188],{"class":5509},[5384,10324,10325],{"class":5604},"9",[5384,10327,6019],{"class":5509},[5384,10329,7325],{"class":5604},[5384,10331,6019],{"class":5509},[5384,10333,10334],{"class":5604},"11",[5384,10336,6019],{"class":5509},[5384,10338,9060],{"class":5604},[5384,10340,6219],{"class":5509},[5384,10342,10343],{"class":5386,"line":6273},[5384,10344,6039],{"class":5509},[5384,10346,10347,10349,10351,10353,10355,10357,10359,10361,10363,10365,10367],{"class":5386,"line":6713},[5384,10348,5523],{"class":5390},[5384,10350,5526],{"class":5509},[5384,10352,5529],{"class":5428},[5384,10354,6234],{"class":5394},[5384,10356,6238],{"class":6237},[5384,10358,5535],{"class":5428},[5384,10360,6243],{"class":5509},[5384,10362,5545],{"class":5428},[5384,10364,6238],{"class":6237},[5384,10366,5548],{"class":5394},[5384,10368,5551],{"class":5509},[5384,10370,10371],{"class":5386,"line":6737},[5384,10372,5517],{"emptyLinePlaceholder":5516},[5384,10374,10375],{"class":5386,"line":6760},[5384,10376,10377],{"class":6008},"# Доступ до одного елемента\n",[5384,10379,10380,10382,10384,10386,10389,10391,10394,10396,10398,10400,10403,10405,10407,10409],{"class":5386,"line":7162},[5384,10381,5523],{"class":5390},[5384,10383,5526],{"class":5509},[5384,10385,5529],{"class":5428},[5384,10387,10388],{"class":5394},"\"Елемент [1, 2]: ",[5384,10390,5535],{"class":5428},[5384,10392,10393],{"class":5509},"matrix[",[5384,10395,5493],{"class":5604},[5384,10397,6019],{"class":5509},[5384,10399,6022],{"class":5604},[5384,10401,10402],{"class":5509},"]",[5384,10404,5545],{"class":5428},[5384,10406,5548],{"class":5394},[5384,10408,8815],{"class":5509},[5384,10410,10411],{"class":6008},"# рядок 1, стовпець 2 → 7\n",[5384,10413,10414],{"class":5386,"line":7172},[5384,10415,5517],{"emptyLinePlaceholder":5516},[5384,10417,10418],{"class":5386,"line":9675},[5384,10419,10420],{"class":6008},"# Доступ до рядка\n",[5384,10422,10423,10425,10427,10429,10432,10434,10436,10438,10440,10442,10444,10446],{"class":5386,"line":9681},[5384,10424,5523],{"class":5390},[5384,10426,5526],{"class":5509},[5384,10428,5529],{"class":5428},[5384,10430,10431],{"class":5394},"\"Другий рядок: ",[5384,10433,5535],{"class":5428},[5384,10435,10393],{"class":5509},[5384,10437,5493],{"class":5604},[5384,10439,10402],{"class":5509},[5384,10441,5545],{"class":5428},[5384,10443,5548],{"class":5394},[5384,10445,8713],{"class":5509},[5384,10447,10448],{"class":6008},"# [5, 6, 7, 8]\n",[5384,10450,10451],{"class":5386,"line":9687},[5384,10452,5517],{"emptyLinePlaceholder":5516},[5384,10454,10455],{"class":5386,"line":9710},[5384,10456,10457],{"class":6008},"# Доступ до стовпця\n",[5384,10459,10460,10462,10464,10466,10469,10471,10474,10476,10478,10480,10482,10484],{"class":5386,"line":9732},[5384,10461,5523],{"class":5390},[5384,10463,5526],{"class":5509},[5384,10465,5529],{"class":5428},[5384,10467,10468],{"class":5394},"\"Третій стовпець: ",[5384,10470,5535],{"class":5428},[5384,10472,10473],{"class":5509},"matrix[:, ",[5384,10475,6022],{"class":5604},[5384,10477,10402],{"class":5509},[5384,10479,5545],{"class":5428},[5384,10481,5548],{"class":5394},[5384,10483,8841],{"class":5509},[5384,10485,10486],{"class":6008},"# [3, 7, 11]\n",[5384,10488,10490],{"class":5386,"line":10489},19,[5384,10491,5517],{"emptyLinePlaceholder":5516},[5384,10493,10495],{"class":5386,"line":10494},20,[5384,10496,10497],{"class":6008},"# Зріз (slicing)\n",[5384,10499,10501,10503,10505,10507,10510,10512,10514,10516,10518,10520,10522,10524,10526,10528,10530,10532,10534,10536],{"class":5386,"line":10500},21,[5384,10502,5523],{"class":5390},[5384,10504,5526],{"class":5509},[5384,10506,5529],{"class":5428},[5384,10508,10509],{"class":5394},"\"Підматриця [0:2, 1:3]:",[5384,10511,6238],{"class":6237},[5384,10513,5535],{"class":5428},[5384,10515,10393],{"class":5509},[5384,10517,5605],{"class":5604},[5384,10519,9443],{"class":5509},[5384,10521,6022],{"class":5604},[5384,10523,6019],{"class":5509},[5384,10525,5493],{"class":5604},[5384,10527,9443],{"class":5509},[5384,10529,5743],{"class":5604},[5384,10531,10402],{"class":5509},[5384,10533,5545],{"class":5428},[5384,10535,5548],{"class":5394},[5384,10537,5551],{"class":5509},[5615,10539,10540],{"v-slot:output":5379},[5618,10541,10542,10556],{},[5621,10543,10544],{},[5624,10545,10546,10549,10551,10553],{},[5627,10547,10548],{"align":5629},"Зріз \u002F Індекс",[5627,10550,5633],{"align":5629},[5627,10552,5777],{"align":5636},[5627,10554,10555],{"align":5629},"Семантика зрізу",[5639,10557,10558,10577,10597,10617,10636],{},[5624,10559,10560,10565,10569,10574],{},[5644,10561,10562],{"align":5629},[5381,10563,10564],{},"matrix[0, 1]",[5644,10566,10567],{"align":5629},[5381,10568,6022],{},[5644,10570,10571,10572,5227],{"align":5636},"Скаляр (",[5381,10573,5787],{},[5644,10575,10576],{"align":5629},"Елемент у рядку 0, стовпчику 1",[5624,10578,10579,10584,10589,10594],{},[5644,10580,10581],{"align":5629},[5381,10582,10583],{},"matrix[0, :]",[5644,10585,10586],{"align":5629},[5381,10587,10588],{},"tensor([1, 2, 3, 4])",[5644,10590,10591],{"align":5636},[5381,10592,10593],{},"torch.Size([4])",[5644,10595,10596],{"align":5629},"Перший рядок матриці",[5624,10598,10599,10604,10609,10614],{},[5644,10600,10601],{"align":5629},[5381,10602,10603],{},"matrix[:, 1]",[5644,10605,10606],{"align":5629},[5381,10607,10608],{},"tensor([2, 6, 10])",[5644,10610,10611],{"align":5636},[5381,10612,10613],{},"torch.Size([3])",[5644,10615,10616],{"align":5629},"Другий стовпчик матриці",[5624,10618,10619,10624,10629,10633],{},[5644,10620,10621],{"align":5629},[5381,10622,10623],{},"matrix[0:2, 1:3]",[5644,10625,10626],{"align":5629},[5381,10627,10628],{},"tensor([[2, 3], [6, 7]])",[5644,10630,10631],{"align":5636},[5381,10632,7297],{},[5644,10634,10635],{"align":5629},"Підматриця $2 \\times 2$",[5624,10637,10638,10643,10648,10653],{},[5644,10639,10640],{"align":5629},[5381,10641,10642],{},"matrix[::2, :]",[5644,10644,10645],{"align":5629},[5381,10646,10647],{},"tensor([[1, 2, 3, 4], [9, 10, 11, 12]])",[5644,10649,10650],{"align":5636},[5381,10651,10652],{},"torch.Size([2, 4])",[5644,10654,10655],{"align":5629},"Рядки з кроком 2 (парні індекси)",[5979,10657,10659],{"id":10658},"булеві-маски-fancy-indexing","Булеві маски (fancy indexing)",[5262,10661,10662],{},"Булеві маски дозволяють фільтрувати елементи за умовою:",[5490,10664,10666,10959],{"title":10665},"boolean_masking.ipynb",[5987,10667,10668,10819],{"execution-count":5493},[5374,10669,10671],{"className":5496,"code":10670,"language":5498,"meta":5379,"style":5379},"import torch\n\nscores = torch.tensor([45, 78, 92, 34, 88, 67])\nprint(f\"Оцінки: {scores}\\n\")\n\n# Створення булевої маски\nmask = scores >= 70\nprint(f\"Маска (оцінка >= 70): {mask}\\n\")\n\n# Вибірка елементів за маскою\npassed = scores[mask]\nprint(f\"Студенти, які склали: {passed}\")\n",[5381,10672,10673,10679,10683,10718,10742,10746,10751,10759,10783,10787,10792,10797],{"__ignoreMap":5379},[5384,10674,10675,10677],{"class":5386,"line":5387},[5384,10676,5506],{"class":5505},[5384,10678,5510],{"class":5509},[5384,10680,10681],{"class":5386,"line":5513},[5384,10682,5517],{"emptyLinePlaceholder":5516},[5384,10684,10685,10688,10691,10693,10696,10698,10701,10703,10706,10708,10711,10713,10716],{"class":5386,"line":5520},[5384,10686,10687],{"class":5509},"scores = torch.tensor([",[5384,10689,10690],{"class":5604},"45",[5384,10692,6019],{"class":5509},[5384,10694,10695],{"class":5604},"78",[5384,10697,6019],{"class":5509},[5384,10699,10700],{"class":5604},"92",[5384,10702,6019],{"class":5509},[5384,10704,10705],{"class":5604},"34",[5384,10707,6019],{"class":5509},[5384,10709,10710],{"class":5604},"88",[5384,10712,6019],{"class":5509},[5384,10714,10715],{"class":5604},"67",[5384,10717,6039],{"class":5509},[5384,10719,10720,10722,10724,10726,10729,10731,10734,10736,10738,10740],{"class":5386,"line":5554},[5384,10721,5523],{"class":5390},[5384,10723,5526],{"class":5509},[5384,10725,5529],{"class":5428},[5384,10727,10728],{"class":5394},"\"Оцінки: ",[5384,10730,5535],{"class":5428},[5384,10732,10733],{"class":5509},"scores",[5384,10735,5545],{"class":5428},[5384,10737,6238],{"class":6237},[5384,10739,5548],{"class":5394},[5384,10741,5551],{"class":5509},[5384,10743,10744],{"class":5386,"line":5577},[5384,10745,5517],{"emptyLinePlaceholder":5516},[5384,10747,10748],{"class":5386,"line":5586},[5384,10749,10750],{"class":6008},"# Створення булевої маски\n",[5384,10752,10753,10756],{"class":5386,"line":6086},[5384,10754,10755],{"class":5509},"mask = scores >= ",[5384,10757,10758],{"class":5604},"70\n",[5384,10760,10761,10763,10765,10767,10770,10772,10775,10777,10779,10781],{"class":5386,"line":6273},[5384,10762,5523],{"class":5390},[5384,10764,5526],{"class":5509},[5384,10766,5529],{"class":5428},[5384,10768,10769],{"class":5394},"\"Маска (оцінка >= 70): ",[5384,10771,5535],{"class":5428},[5384,10773,10774],{"class":5509},"mask",[5384,10776,5545],{"class":5428},[5384,10778,6238],{"class":6237},[5384,10780,5548],{"class":5394},[5384,10782,5551],{"class":5509},[5384,10784,10785],{"class":5386,"line":6713},[5384,10786,5517],{"emptyLinePlaceholder":5516},[5384,10788,10789],{"class":5386,"line":6737},[5384,10790,10791],{"class":6008},"# Вибірка елементів за маскою\n",[5384,10793,10794],{"class":5386,"line":6760},[5384,10795,10796],{"class":5509},"passed = scores[mask]\n",[5384,10798,10799,10801,10803,10805,10808,10810,10813,10815,10817],{"class":5386,"line":7162},[5384,10800,5523],{"class":5390},[5384,10802,5526],{"class":5509},[5384,10804,5529],{"class":5428},[5384,10806,10807],{"class":5394},"\"Студенти, які склали: ",[5384,10809,5535],{"class":5428},[5384,10811,10812],{"class":5509},"passed",[5384,10814,5545],{"class":5428},[5384,10816,5548],{"class":5394},[5384,10818,5551],{"class":5509},[5615,10820,10821],{"v-slot:output":5379},[5618,10822,10823,10839],{},[5621,10824,10825],{},[5624,10826,10827,10830,10833,10836],{},[5627,10828,10829],{"align":5636},"Студент",[5627,10831,10832],{"align":5636},"Оцінка",[5627,10834,10835],{"align":5636},"Умова ($\\ge 70$)",[5627,10837,10838],{"align":5629},"Результат фільтрації",[5639,10840,10841,10857,10875,10892,10907,10924,10939],{},[5624,10842,10843,10846,10850,10854],{},[5644,10844,10845],{"align":5636},"Студент 1",[5644,10847,10848],{"align":5636},[5381,10849,10690],{},[5644,10851,10852],{"align":5636},[5381,10853,9818],{},[5644,10855,10856],{"align":5629},"❌ Відсіяно",[5624,10858,10859,10862,10866,10870],{},[5644,10860,10861],{"align":5636},"Студент 2",[5644,10863,10864],{"align":5636},[5381,10865,10695],{},[5644,10867,10868],{"align":5636},[5381,10869,5668],{},[5644,10871,10872,10873],{"align":5629},"✅ ",[5381,10874,10695],{},[5624,10876,10877,10880,10884,10888],{},[5644,10878,10879],{"align":5636},"Студент 3",[5644,10881,10882],{"align":5636},[5381,10883,10700],{},[5644,10885,10886],{"align":5636},[5381,10887,5668],{},[5644,10889,10872,10890],{"align":5629},[5381,10891,10700],{},[5624,10893,10894,10897,10901,10905],{},[5644,10895,10896],{"align":5636},"Студент 4",[5644,10898,10899],{"align":5636},[5381,10900,10705],{},[5644,10902,10903],{"align":5636},[5381,10904,9818],{},[5644,10906,10856],{"align":5629},[5624,10908,10909,10912,10916,10920],{},[5644,10910,10911],{"align":5636},"Студент 5",[5644,10913,10914],{"align":5636},[5381,10915,10710],{},[5644,10917,10918],{"align":5636},[5381,10919,5668],{},[5644,10921,10872,10922],{"align":5629},[5381,10923,10710],{},[5624,10925,10926,10929,10933,10937],{},[5644,10927,10928],{"align":5636},"Студент 6",[5644,10930,10931],{"align":5636},[5381,10932,10715],{},[5644,10934,10935],{"align":5636},[5381,10936,9818],{},[5644,10938,10856],{"align":5629},[5624,10940,10941,10946,10949,10951],{},[5644,10942,10943],{"align":5636},[5220,10944,10945],{},"Успішні студенти:",[5644,10947,10948],{"align":5636},"—",[5644,10950,10948],{"align":5636},[5644,10952,10953,10958],{"align":5629},[5220,10954,10955],{},[5381,10956,10957],{},"tensor([78, 92, 88])"," (3 особи)",[5987,10960,10961,11099],{"execution-count":6022},[5374,10962,10964],{"className":5496,"code":10963,"language":5498,"meta":5379,"style":5379},"# torch.where() — аналог NumPy np.where\ntemperatures = torch.tensor([28, 15, 32, 19, 35, 22])\n\n# Якщо температура > 30, повернути \"Спека\", інакше — \"Нормально\"\nstatus = torch.where(temperatures > 30, \n                     torch.tensor(1),  # \"Спека\" \n                     torch.tensor(0))  # \"Нормально\"\n\nprint(f\"Температури: {temperatures}\")\nprint(f\"Статус (1=Спека, 0=Норм): {status}\")\n",[5381,10965,10966,10971,11006,11010,11015,11026,11039,11051,11055,11077],{"__ignoreMap":5379},[5384,10967,10968],{"class":5386,"line":5387},[5384,10969,10970],{"class":6008},"# torch.where() — аналог NumPy np.where\n",[5384,10972,10973,10976,10979,10981,10984,10986,10989,10991,10994,10996,10999,11001,11004],{"class":5386,"line":5513},[5384,10974,10975],{"class":5509},"temperatures = torch.tensor([",[5384,10977,10978],{"class":5604},"28",[5384,10980,6019],{"class":5509},[5384,10982,10983],{"class":5604},"15",[5384,10985,6019],{"class":5509},[5384,10987,10988],{"class":5604},"32",[5384,10990,6019],{"class":5509},[5384,10992,10993],{"class":5604},"19",[5384,10995,6019],{"class":5509},[5384,10997,10998],{"class":5604},"35",[5384,11000,6019],{"class":5509},[5384,11002,11003],{"class":5604},"22",[5384,11005,6039],{"class":5509},[5384,11007,11008],{"class":5386,"line":5520},[5384,11009,5517],{"emptyLinePlaceholder":5516},[5384,11011,11012],{"class":5386,"line":5554},[5384,11013,11014],{"class":6008},"# Якщо температура > 30, повернути \"Спека\", інакше — \"Нормально\"\n",[5384,11016,11017,11020,11023],{"class":5386,"line":5577},[5384,11018,11019],{"class":5509},"status = torch.where(temperatures > ",[5384,11021,11022],{"class":5604},"30",[5384,11024,11025],{"class":5509},", \n",[5384,11027,11028,11031,11033,11036],{"class":5386,"line":5586},[5384,11029,11030],{"class":5509},"                     torch.tensor(",[5384,11032,5493],{"class":5604},[5384,11034,11035],{"class":5509},"),  ",[5384,11037,11038],{"class":6008},"# \"Спека\" \n",[5384,11040,11041,11043,11045,11048],{"class":5386,"line":6086},[5384,11042,11030],{"class":5509},[5384,11044,5605],{"class":5604},[5384,11046,11047],{"class":5509},"))  ",[5384,11049,11050],{"class":6008},"# \"Нормально\"\n",[5384,11052,11053],{"class":5386,"line":6273},[5384,11054,5517],{"emptyLinePlaceholder":5516},[5384,11056,11057,11059,11061,11063,11066,11068,11071,11073,11075],{"class":5386,"line":6713},[5384,11058,5523],{"class":5390},[5384,11060,5526],{"class":5509},[5384,11062,5529],{"class":5428},[5384,11064,11065],{"class":5394},"\"Температури: ",[5384,11067,5535],{"class":5428},[5384,11069,11070],{"class":5509},"temperatures",[5384,11072,5545],{"class":5428},[5384,11074,5548],{"class":5394},[5384,11076,5551],{"class":5509},[5384,11078,11079,11081,11083,11085,11088,11090,11093,11095,11097],{"class":5386,"line":6737},[5384,11080,5523],{"class":5390},[5384,11082,5526],{"class":5509},[5384,11084,5529],{"class":5428},[5384,11086,11087],{"class":5394},"\"Статус (1=Спека, 0=Норм): ",[5384,11089,5535],{"class":5428},[5384,11091,11092],{"class":5509},"status",[5384,11094,5545],{"class":5428},[5384,11096,5548],{"class":5394},[5384,11098,5551],{"class":5509},[5615,11100,11101],{"v-slot:output":5379},[5618,11102,11103,11128],{},[5621,11104,11105],{},[5624,11106,11107,11110,11113,11116,11122],{},[5627,11108,11109],{"align":5636},"День",[5627,11111,11112],{"align":5636},"Температура (°C)",[5627,11114,11115],{"align":5636},"Умова ($T > 25$)",[5627,11117,11118,11119,5227],{"align":5636},"Код статусу (",[5381,11120,11121],{},"torch.where",[5627,11123,11124,11125,6523],{"align":5636},"Обрізані значення $",[5384,11126,11127],{},"18, 30",[5639,11129,11130,11151,11174,11195,11215,11235],{},[5624,11131,11132,11134,11138,11142,11147],{},[5644,11133,5493],{"align":5636},[5644,11135,11136],{"align":5636},[5381,11137,10978],{},[5644,11139,11140],{"align":5636},[5381,11141,5668],{},[5644,11143,11144,11146],{"align":5636},[5220,11145,5493],{}," (☀️ Спекотно)",[5644,11148,11149],{"align":5636},[5381,11150,10978],{},[5624,11152,11153,11155,11159,11163,11168],{},[5644,11154,6022],{"align":5636},[5644,11156,11157],{"align":5636},[5381,11158,10983],{},[5644,11160,11161],{"align":5636},[5381,11162,9818],{},[5644,11164,11165,11167],{"align":5636},[5220,11166,5605],{}," (⛅ Норма)",[5644,11169,11170,11173],{"align":5636},[5381,11171,11172],{},"18"," (підтягнуто до min)",[5624,11175,11176,11178,11182,11186,11190],{},[5644,11177,5743],{"align":5636},[5644,11179,11180],{"align":5636},[5381,11181,10988],{},[5644,11183,11184],{"align":5636},[5381,11185,5668],{},[5644,11187,11188,11146],{"align":5636},[5220,11189,5493],{},[5644,11191,11192,11194],{"align":5636},[5381,11193,11022],{}," (обрізано до max)",[5624,11196,11197,11199,11203,11207,11211],{},[5644,11198,6031],{"align":5636},[5644,11200,11201],{"align":5636},[5381,11202,10993],{},[5644,11204,11205],{"align":5636},[5381,11206,9818],{},[5644,11208,11209,11167],{"align":5636},[5220,11210,5605],{},[5644,11212,11213],{"align":5636},[5381,11214,10993],{},[5624,11216,11217,11219,11223,11227,11231],{},[5644,11218,6036],{"align":5636},[5644,11220,11221],{"align":5636},[5381,11222,10998],{},[5644,11224,11225],{"align":5636},[5381,11226,5668],{},[5644,11228,11229,11146],{"align":5636},[5220,11230,5493],{},[5644,11232,11233,11194],{"align":5636},[5381,11234,11022],{},[5624,11236,11237,11239,11243,11247,11251],{},[5644,11238,6216],{"align":5636},[5644,11240,11241],{"align":5636},[5381,11242,11003],{},[5644,11244,11245],{"align":5636},[5381,11246,9818],{},[5644,11248,11249,11167],{"align":5636},[5220,11250,5605],{},[5644,11252,11253],{"align":5636},[5381,11254,11003],{},[5351,11256,11257],{},[5262,11258,11259,11261],{},[5220,11260,5357],{}," Булеві маски — це як фільтри в Excel: ви задаєте умову (наприклад, \"показати лише рядки, де оцінка >= 70\"), і таблиця автоматично приховує всі інші рядки.",[5688,11263],{},[5979,11265,11267],{"id":11266},"broadcasting-автоматичне-розширення-форм","Broadcasting — автоматичне розширення форм",[5262,11269,11270,11272],{},[5220,11271,5248],{}," — це потужний механізм, який дозволяє виконувати операції над тензорами різних форм без явного копіювання даних. PyTorch автоматично розширює менший тензор до форми більшого.",[5262,11274,11275],{},[5268,11276],{"alt":11277,"className":11278,"src":11279},"Геометричний механізм трансляції форм (Broadcasting) без дублювання фізичної пам'яті",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F05.png",[5275,11281,11283,11285,11287,11294,11304,11311,11314],{"alt":11282},"Механізм Broadcasting при операціях з різними формами",[5262,11284,5280],{},[5262,11286,5283],{},[5262,11288,11289,11290,11293],{},"rectangle \"Тензор A (Матриця 2x3)\" as A #EFF6FF {\ncard \"[[1, 2, 3],\\n ",[5384,11291,11292],{},"4, 5, 6","]\" as a_data\n}",[5262,11295,11296,11297,11300,11301,11303],{},"rectangle \"Тензор B (Вектор-рядок 1x3)\" as B #FEF3C7 {\ncard \"",[5384,11298,11299],{},"10, 20, 30","\" as b_orig\ncard \"Віртуальне копіювання по рядках:\\n[[10, 20, 30],\\n ",[5384,11302,11299],{},"]\" as b_broadcast\nb_orig -down-> b_broadcast : Автоматичне розширення\n}",[5262,11305,11306,11307,11310],{},"rectangle \"Результат: A + B (Матриця 2x3)\" as RES #ECFDF5 {\ncard \"[[11, 22, 33],\\n ",[5384,11308,11309],{},"14, 25, 36","]\" as res_data\n}",[5262,11312,11313],{},"A -right-> RES : +\nb_broadcast -right-> RES : +",[5262,11315,5292],{},[5490,11317,11319,11493],{"title":11318},"broadcasting_intro.ipynb",[5987,11320,11321,11425],{"execution-count":5493},[5374,11322,11324],{"className":5496,"code":11323,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Додавання скаляра до вектора\nvector = torch.tensor([1, 2, 3, 4])\nresult = vector + 10\n\nprint(f\"Вектор: {vector}\")\nprint(f\"Вектор + 10: {result}\")\nprint(\"Broadcasting: скаляр 10 розширюється до [10, 10, 10, 10]\")\n",[5381,11325,11326,11332,11336,11341,11361,11369,11373,11393,11414],{"__ignoreMap":5379},[5384,11327,11328,11330],{"class":5386,"line":5387},[5384,11329,5506],{"class":5505},[5384,11331,5510],{"class":5509},[5384,11333,11334],{"class":5386,"line":5513},[5384,11335,5517],{"emptyLinePlaceholder":5516},[5384,11337,11338],{"class":5386,"line":5520},[5384,11339,11340],{"class":6008},"# Додавання скаляра до вектора\n",[5384,11342,11343,11345,11347,11349,11351,11353,11355,11357,11359],{"class":5386,"line":5554},[5384,11344,6014],{"class":5509},[5384,11346,5493],{"class":5604},[5384,11348,6019],{"class":5509},[5384,11350,6022],{"class":5604},[5384,11352,6019],{"class":5509},[5384,11354,5743],{"class":5604},[5384,11356,6019],{"class":5509},[5384,11358,6031],{"class":5604},[5384,11360,6039],{"class":5509},[5384,11362,11363,11366],{"class":5386,"line":5577},[5384,11364,11365],{"class":5509},"result = vector + ",[5384,11367,11368],{"class":5604},"10\n",[5384,11370,11371],{"class":5386,"line":5586},[5384,11372,5517],{"emptyLinePlaceholder":5516},[5384,11374,11375,11377,11379,11381,11383,11385,11387,11389,11391],{"class":5386,"line":6086},[5384,11376,5523],{"class":5390},[5384,11378,5526],{"class":5509},[5384,11380,5529],{"class":5428},[5384,11382,6050],{"class":5394},[5384,11384,5535],{"class":5428},[5384,11386,6055],{"class":5509},[5384,11388,5545],{"class":5428},[5384,11390,5548],{"class":5394},[5384,11392,5551],{"class":5509},[5384,11394,11395,11397,11399,11401,11404,11406,11408,11410,11412],{"class":5386,"line":6273},[5384,11396,5523],{"class":5390},[5384,11398,5526],{"class":5509},[5384,11400,5529],{"class":5428},[5384,11402,11403],{"class":5394},"\"Вектор + 10: ",[5384,11405,5535],{"class":5428},[5384,11407,10014],{"class":5509},[5384,11409,5545],{"class":5428},[5384,11411,5548],{"class":5394},[5384,11413,5551],{"class":5509},[5384,11415,11416,11418,11420,11423],{"class":5386,"line":6713},[5384,11417,5523],{"class":5390},[5384,11419,5526],{"class":5509},[5384,11421,11422],{"class":5394},"\"Broadcasting: скаляр 10 розширюється до [10, 10, 10, 10]\"",[5384,11424,5551],{"class":5509},[5615,11426,11427],{"v-slot:output":5379},[5618,11428,11429,11444],{},[5621,11430,11431],{},[5624,11432,11433,11436,11438,11441],{},[5627,11434,11435],{"align":5629},"Вихідний вектор",[5627,11437,5873],{"align":5629},[5627,11439,11440],{"align":5629},"Результат broadcasting",[5627,11442,11443],{"align":5629},"Механізм розширення",[5639,11445,11446,11471],{},[5624,11447,11448,11452,11457,11462],{},[5644,11449,11450],{"align":5629},[5381,11451,10588],{},[5644,11453,11454],{"align":5629},[5381,11455,11456],{},"+ 10",[5644,11458,11459],{"align":5629},[5381,11460,11461],{},"tensor([11, 12, 13, 14])",[5644,11463,11464,11465,11467,11468],{"align":5629},"Скаляр ",[5381,11466,7325],{}," розширено до розміру ",[5381,11469,11470],{},"(4,)",[5624,11472,11473,11477,11482,11487],{},[5644,11474,11475],{"align":5629},[5381,11476,10588],{},[5644,11478,11479],{"align":5629},[5381,11480,11481],{},"* 2",[5644,11483,11484],{"align":5629},[5381,11485,11486],{},"tensor([2, 4, 6, 8])",[5644,11488,11464,11489,11467,11491],{"align":5629},[5381,11490,6022],{},[5381,11492,11470],{},[5987,11494,11495,11657],{"execution-count":6022},[5374,11496,11498],{"className":5496,"code":11497,"language":5498,"meta":5379,"style":5379},"# Додавання вектора до матриці\nmatrix = torch.tensor([\n    [1, 2, 3],\n    [4, 5, 6]\n])\nvector = torch.tensor([10, 20, 30])\n\nresult = matrix + vector\nprint(f\"Матриця (2×3):\\n{matrix}\\n\")\nprint(f\"Вектор (3,): {vector}\\n\")\nprint(f\"Результат:\\n{result}\")\nprint(\"\\nBroadcasting: вектор розширюється до матриці [[10,20,30], [10,20,30]]\")\n",[5381,11499,11500,11505,11509,11525,11541,11545,11562,11566,11571,11596,11619,11642],{"__ignoreMap":5379},[5384,11501,11502],{"class":5386,"line":5387},[5384,11503,11504],{"class":6008},"# Додавання вектора до матриці\n",[5384,11506,11507],{"class":5386,"line":5513},[5384,11508,6183],{"class":5509},[5384,11510,11511,11513,11515,11517,11519,11521,11523],{"class":5386,"line":5520},[5384,11512,6188],{"class":5509},[5384,11514,5493],{"class":5604},[5384,11516,6019],{"class":5509},[5384,11518,6022],{"class":5604},[5384,11520,6019],{"class":5509},[5384,11522,5743],{"class":5604},[5384,11524,6201],{"class":5509},[5384,11526,11527,11529,11531,11533,11535,11537,11539],{"class":5386,"line":5554},[5384,11528,6188],{"class":5509},[5384,11530,6031],{"class":5604},[5384,11532,6019],{"class":5509},[5384,11534,6036],{"class":5604},[5384,11536,6019],{"class":5509},[5384,11538,6216],{"class":5604},[5384,11540,6219],{"class":5509},[5384,11542,11543],{"class":5386,"line":5577},[5384,11544,6039],{"class":5509},[5384,11546,11547,11549,11551,11553,11556,11558,11560],{"class":5386,"line":5586},[5384,11548,6014],{"class":5509},[5384,11550,7325],{"class":5604},[5384,11552,6019],{"class":5509},[5384,11554,11555],{"class":5604},"20",[5384,11557,6019],{"class":5509},[5384,11559,11022],{"class":5604},[5384,11561,6039],{"class":5509},[5384,11563,11564],{"class":5386,"line":6086},[5384,11565,5517],{"emptyLinePlaceholder":5516},[5384,11567,11568],{"class":5386,"line":6273},[5384,11569,11570],{"class":5509},"result = matrix + vector\n",[5384,11572,11573,11575,11577,11579,11582,11584,11586,11588,11590,11592,11594],{"class":5386,"line":6713},[5384,11574,5523],{"class":5390},[5384,11576,5526],{"class":5509},[5384,11578,5529],{"class":5428},[5384,11580,11581],{"class":5394},"\"Матриця (2×3):",[5384,11583,6238],{"class":6237},[5384,11585,5535],{"class":5428},[5384,11587,6243],{"class":5509},[5384,11589,5545],{"class":5428},[5384,11591,6238],{"class":6237},[5384,11593,5548],{"class":5394},[5384,11595,5551],{"class":5509},[5384,11597,11598,11600,11602,11604,11607,11609,11611,11613,11615,11617],{"class":5386,"line":6737},[5384,11599,5523],{"class":5390},[5384,11601,5526],{"class":5509},[5384,11603,5529],{"class":5428},[5384,11605,11606],{"class":5394},"\"Вектор (3,): ",[5384,11608,5535],{"class":5428},[5384,11610,6055],{"class":5509},[5384,11612,5545],{"class":5428},[5384,11614,6238],{"class":6237},[5384,11616,5548],{"class":5394},[5384,11618,5551],{"class":5509},[5384,11620,11621,11623,11625,11627,11630,11632,11634,11636,11638,11640],{"class":5386,"line":6760},[5384,11622,5523],{"class":5390},[5384,11624,5526],{"class":5509},[5384,11626,5529],{"class":5428},[5384,11628,11629],{"class":5394},"\"Результат:",[5384,11631,6238],{"class":6237},[5384,11633,5535],{"class":5428},[5384,11635,10014],{"class":5509},[5384,11637,5545],{"class":5428},[5384,11639,5548],{"class":5394},[5384,11641,5551],{"class":5509},[5384,11643,11644,11646,11648,11650,11652,11655],{"class":5386,"line":7162},[5384,11645,5523],{"class":5390},[5384,11647,5526],{"class":5509},[5384,11649,5548],{"class":5394},[5384,11651,6238],{"class":6237},[5384,11653,11654],{"class":5394},"Broadcasting: вектор розширюється до матриці [[10,20,30], [10,20,30]]\"",[5384,11656,5551],{"class":5509},[5615,11658,11659],{"v-slot:output":5379},[5618,11660,11661,11675],{},[5621,11662,11663],{},[5624,11664,11665,11668,11670,11672],{},[5627,11666,11667],{"align":5629},"Операнд",[5627,11669,6526],{"align":5636},[5627,11671,5633],{"align":5629},[5627,11673,11674],{"align":5629},"Поведінка Broadcasting",[5639,11676,11677,11695,11717],{},[5624,11678,11679,11684,11688,11692],{},[5644,11680,11681],{"align":5629},[5220,11682,11683],{},"Матриця $A$",[5644,11685,11686],{"align":5636},[5381,11687,6341],{},[5644,11689,11690],{"align":5629},[5381,11691,6820],{},[5644,11693,11694],{"align":5629},"Базовий операнд",[5624,11696,11697,11702,11706,11711],{},[5644,11698,11699],{"align":5629},[5220,11700,11701],{},"Вектор $b$",[5644,11703,11704],{"align":5636},[5381,11705,10613],{},[5644,11707,11708],{"align":5629},[5381,11709,11710],{},"[10, 20, 30]",[5644,11712,11713,11714],{"align":5629},"Автоматично транслюється до форми ",[5381,11715,11716],{},"(2, 3)",[5624,11718,11719,11724,11728,11733],{},[5644,11720,11721],{"align":5629},[5220,11722,11723],{},"Результат $A + b$",[5644,11725,11726],{"align":5636},[5381,11727,6341],{},[5644,11729,11730],{"align":5629},[5381,11731,11732],{},"[[11, 22, 33], [14, 25, 36]]",[5644,11734,11735],{"align":5629},"Поелементне додавання кожного рядка",[5351,11737,11738],{},[5262,11739,11740,11742],{},[5220,11741,5357],{}," Уявіть, що ви маляр і вам потрібно пофарбувати 5 стін у один колір. Замість того, щоб змішувати фарбу окремо для кожної стіни (копіювання даних), ви використовуєте одну банку для всіх (broadcasting).",[5262,11744,11745,11748],{},[5220,11746,11747],{},"Правила broadcasting"," (такі ж, як у NumPy):",[10217,11750,11751,11754],{},[5197,11752,11753],{},"Вирівнюємо форми праворуч, додаючи одиниці зліва до коротшої форми",[5197,11755,11756,11757],{},"Порівнюємо розміри по кожній вісі:\n",[5194,11758,11759,11762,11765],{},[5197,11760,11761],{},"Якщо розміри рівні — OK",[5197,11763,11764],{},"Якщо один з розмірів = 1 — розтягуємо його",[5197,11766,11767],{},"Інакше — помилка",[5490,11769,11771],{"title":11770},"broadcasting_rules.ipynb",[5987,11772,11773,11990],{"execution-count":5493},[5374,11774,11776],{"className":5496,"code":11775,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Приклад 1: Сумісні форми\na = torch.ones(3, 1)      # форма (3, 1)\nb = torch.ones(1, 4)      # форма (1, 4)\nresult = a + b            # результат (3, 4)\n\nprint(f\"a форма: {a.shape} → розтягнеться до (3, 4)\")\nprint(f\"b форма: {b.shape} → розтягнеться до (3, 4)\")\nprint(f\"Результат форма: {result.shape}\\n\")\n\n# Приклад 2: Несумісні форми (помилка)\ntry:\n    a = torch.ones(3, 2)\n    b = torch.ones(3, 3)\n    result = a + b\nexcept RuntimeError as e:\n    print(f\"Помилка: {e}\")\n",[5381,11777,11778,11784,11788,11793,11809,11825,11833,11837,11860,11882,11906,11910,11915,11922,11935,11948,11953,11968],{"__ignoreMap":5379},[5384,11779,11780,11782],{"class":5386,"line":5387},[5384,11781,5506],{"class":5505},[5384,11783,5510],{"class":5509},[5384,11785,11786],{"class":5386,"line":5513},[5384,11787,5517],{"emptyLinePlaceholder":5516},[5384,11789,11790],{"class":5386,"line":5520},[5384,11791,11792],{"class":6008},"# Приклад 1: Сумісні форми\n",[5384,11794,11795,11798,11800,11802,11804,11806],{"class":5386,"line":5554},[5384,11796,11797],{"class":5509},"a = torch.ones(",[5384,11799,5743],{"class":5604},[5384,11801,6019],{"class":5509},[5384,11803,5493],{"class":5604},[5384,11805,8815],{"class":5509},[5384,11807,11808],{"class":6008},"# форма (3, 1)\n",[5384,11810,11811,11814,11816,11818,11820,11822],{"class":5386,"line":5577},[5384,11812,11813],{"class":5509},"b = torch.ones(",[5384,11815,5493],{"class":5604},[5384,11817,6019],{"class":5509},[5384,11819,6031],{"class":5604},[5384,11821,8815],{"class":5509},[5384,11823,11824],{"class":6008},"# форма (1, 4)\n",[5384,11826,11827,11830],{"class":5386,"line":5586},[5384,11828,11829],{"class":5509},"result = a + b            ",[5384,11831,11832],{"class":6008},"# результат (3, 4)\n",[5384,11834,11835],{"class":5386,"line":6086},[5384,11836,5517],{"emptyLinePlaceholder":5516},[5384,11838,11839,11841,11843,11845,11848,11850,11853,11855,11858],{"class":5386,"line":6273},[5384,11840,5523],{"class":5390},[5384,11842,5526],{"class":5509},[5384,11844,5529],{"class":5428},[5384,11846,11847],{"class":5394},"\"a форма: ",[5384,11849,5535],{"class":5428},[5384,11851,11852],{"class":5509},"a.shape",[5384,11854,5545],{"class":5428},[5384,11856,11857],{"class":5394}," → розтягнеться до (3, 4)\"",[5384,11859,5551],{"class":5509},[5384,11861,11862,11864,11866,11868,11871,11873,11876,11878,11880],{"class":5386,"line":6713},[5384,11863,5523],{"class":5390},[5384,11865,5526],{"class":5509},[5384,11867,5529],{"class":5428},[5384,11869,11870],{"class":5394},"\"b форма: ",[5384,11872,5535],{"class":5428},[5384,11874,11875],{"class":5509},"b.shape",[5384,11877,5545],{"class":5428},[5384,11879,11857],{"class":5394},[5384,11881,5551],{"class":5509},[5384,11883,11884,11886,11888,11890,11893,11895,11898,11900,11902,11904],{"class":5386,"line":6737},[5384,11885,5523],{"class":5390},[5384,11887,5526],{"class":5509},[5384,11889,5529],{"class":5428},[5384,11891,11892],{"class":5394},"\"Результат форма: ",[5384,11894,5535],{"class":5428},[5384,11896,11897],{"class":5509},"result.shape",[5384,11899,5545],{"class":5428},[5384,11901,6238],{"class":6237},[5384,11903,5548],{"class":5394},[5384,11905,5551],{"class":5509},[5384,11907,11908],{"class":5386,"line":6760},[5384,11909,5517],{"emptyLinePlaceholder":5516},[5384,11911,11912],{"class":5386,"line":7162},[5384,11913,11914],{"class":6008},"# Приклад 2: Несумісні форми (помилка)\n",[5384,11916,11917,11920],{"class":5386,"line":7172},[5384,11918,11919],{"class":5505},"try",[5384,11921,8499],{"class":5509},[5384,11923,11924,11927,11929,11931,11933],{"class":5386,"line":9675},[5384,11925,11926],{"class":5509},"    a = torch.ones(",[5384,11928,5743],{"class":5604},[5384,11930,6019],{"class":5509},[5384,11932,6022],{"class":5604},[5384,11934,5551],{"class":5509},[5384,11936,11937,11940,11942,11944,11946],{"class":5386,"line":9681},[5384,11938,11939],{"class":5509},"    b = torch.ones(",[5384,11941,5743],{"class":5604},[5384,11943,6019],{"class":5509},[5384,11945,5743],{"class":5604},[5384,11947,5551],{"class":5509},[5384,11949,11950],{"class":5386,"line":9687},[5384,11951,11952],{"class":5509},"    result = a + b\n",[5384,11954,11955,11958,11962,11965],{"class":5386,"line":9710},[5384,11956,11957],{"class":5505},"except",[5384,11959,11961],{"class":11960},"sN1BT"," RuntimeError",[5384,11963,11964],{"class":5505}," as",[5384,11966,11967],{"class":5509}," e:\n",[5384,11969,11970,11972,11974,11976,11979,11981,11984,11986,11988],{"class":5386,"line":9732},[5384,11971,5589],{"class":5390},[5384,11973,5526],{"class":5509},[5384,11975,5529],{"class":5428},[5384,11977,11978],{"class":5394},"\"Помилка: ",[5384,11980,5535],{"class":5428},[5384,11982,11983],{"class":5509},"e",[5384,11985,5545],{"class":5428},[5384,11987,5548],{"class":5394},[5384,11989,5551],{"class":5509},[5615,11991,11992],{"v-slot:output":5379},[5618,11993,11994,12010],{},[5621,11995,11996],{},[5624,11997,11998,12001,12004,12007],{},[5627,11999,12000],{"align":5629},"Тензор",[5627,12002,12003],{"align":5636},"Початкова форма",[5627,12005,12006],{"align":5636},"Розширення вимірів",[5627,12008,12009],{"align":5636},"Фінальна форма для операції",[5639,12011,12012,12034,12054],{},[5624,12013,12014,12019,12024,12029],{},[5644,12015,12016],{"align":5629},[5220,12017,12018],{},"Тензор $A$",[5644,12020,12021],{"align":5636},[5381,12022,12023],{},"torch.Size([3, 1])",[5644,12025,12026],{"align":5636},[5381,12027,12028],{},"[3, 1] -> [3, 4]",[5644,12030,12031],{"align":5636},[5381,12032,12033],{},"(3, 4)",[5624,12035,12036,12041,12045,12050],{},[5644,12037,12038],{"align":5629},[5220,12039,12040],{},"Тензор $B$",[5644,12042,12043],{"align":5636},[5381,12044,10593],{},[5644,12046,12047],{"align":5636},[5381,12048,12049],{},"[1, 4] -> [3, 4]",[5644,12051,12052],{"align":5636},[5381,12053,12033],{},[5624,12055,12056,12061,12063,12065],{},[5644,12057,12058],{"align":5629},[5220,12059,12060],{},"Результат $A + B$",[5644,12062,10948],{"align":5636},[5644,12064,10948],{"align":5636},[5644,12066,12067],{"align":5636},[5220,12068,12069],{},[5381,12070,7234],{},[6588,12072,12073,12078],{},[5262,12074,12075],{},[5220,12076,12077],{},"Практичне правило для broadcasting:",[5194,12079,12080,12093,12101,12110],{},[5197,12081,12082,12085,12086,12089,12090,12092],{},[5220,12083,12084],{},"(n, 1)"," + ",[5220,12087,12088],{},"(1, m)"," → ",[5220,12091,5818],{}," ✅",[5197,12094,12095,12085,12097,12089,12099,12092],{},[5220,12096,5805],{},[5220,12098,5818],{},[5220,12100,5818],{},[5197,12102,12103,12085,12105,12089,12108,12092],{},[5220,12104,5818],{},[5220,12106,12107],{},"(m,)",[5220,12109,5818],{},[5197,12111,12112,12085,12115,12089,12117,12120],{},[5220,12113,12114],{},"(n, k)",[5220,12116,5818],{},[5220,12118,12119],{},"помилка"," ❌ (якщо k ≠ m)",[5979,12122,12124],{"id":12123},"математичні-операції","Математичні операції",[5262,12126,12127],{},"PyTorch підтримує величезний набір математичних операцій. Розглянемо найважливіші:",[12129,12130,12132],"h5",{"id":12131},"поелементні-операції","Поелементні операції",[5490,12134,12136,12470],{"title":12135},"elementwise_ops.ipynb",[5987,12137,12138,12376],{"execution-count":5493},[5374,12139,12141],{"className":5496,"code":12140,"language":5498,"meta":5379,"style":5379},"import torch\n\na = torch.tensor([1.0, 2.0, 3.0, 4.0])\nb = torch.tensor([10.0, 20.0, 30.0, 40.0])\n\nprint(f\"a: {a}\")\nprint(f\"b: {b}\\n\")\n\n# Арифметичні операції\nprint(f\"a + b: {a + b}\")\nprint(f\"a - b: {a - b}\")\nprint(f\"a * b: {a * b}\")  # поелементне множення\nprint(f\"a \u002F b: {a \u002F b}\")\nprint(f\"a ** 2: {a ** 2}\")  # піднесення до степеня\n",[5381,12142,12143,12149,12153,12174,12199,12203,12225,12249,12253,12258,12280,12302,12327,12349],{"__ignoreMap":5379},[5384,12144,12145,12147],{"class":5386,"line":5387},[5384,12146,5506],{"class":5505},[5384,12148,5510],{"class":5509},[5384,12150,12151],{"class":5386,"line":5513},[5384,12152,5517],{"emptyLinePlaceholder":5516},[5384,12154,12155,12158,12160,12162,12164,12166,12168,12170,12172],{"class":5386,"line":5520},[5384,12156,12157],{"class":5509},"a = torch.tensor([",[5384,12159,7252],{"class":5604},[5384,12161,6019],{"class":5509},[5384,12163,8059],{"class":5604},[5384,12165,6019],{"class":5509},[5384,12167,8064],{"class":5604},[5384,12169,6019],{"class":5509},[5384,12171,8445],{"class":5604},[5384,12173,6039],{"class":5509},[5384,12175,12176,12179,12182,12184,12187,12189,12192,12194,12197],{"class":5386,"line":5554},[5384,12177,12178],{"class":5509},"b = torch.tensor([",[5384,12180,12181],{"class":5604},"10.0",[5384,12183,6019],{"class":5509},[5384,12185,12186],{"class":5604},"20.0",[5384,12188,6019],{"class":5509},[5384,12190,12191],{"class":5604},"30.0",[5384,12193,6019],{"class":5509},[5384,12195,12196],{"class":5604},"40.0",[5384,12198,6039],{"class":5509},[5384,12200,12201],{"class":5386,"line":5577},[5384,12202,5517],{"emptyLinePlaceholder":5516},[5384,12204,12205,12207,12209,12211,12214,12216,12219,12221,12223],{"class":5386,"line":5586},[5384,12206,5523],{"class":5390},[5384,12208,5526],{"class":5509},[5384,12210,5529],{"class":5428},[5384,12212,12213],{"class":5394},"\"a: ",[5384,12215,5535],{"class":5428},[5384,12217,12218],{"class":5509},"a",[5384,12220,5545],{"class":5428},[5384,12222,5548],{"class":5394},[5384,12224,5551],{"class":5509},[5384,12226,12227,12229,12231,12233,12236,12238,12241,12243,12245,12247],{"class":5386,"line":6086},[5384,12228,5523],{"class":5390},[5384,12230,5526],{"class":5509},[5384,12232,5529],{"class":5428},[5384,12234,12235],{"class":5394},"\"b: ",[5384,12237,5535],{"class":5428},[5384,12239,12240],{"class":5509},"b",[5384,12242,5545],{"class":5428},[5384,12244,6238],{"class":6237},[5384,12246,5548],{"class":5394},[5384,12248,5551],{"class":5509},[5384,12250,12251],{"class":5386,"line":6273},[5384,12252,5517],{"emptyLinePlaceholder":5516},[5384,12254,12255],{"class":5386,"line":6713},[5384,12256,12257],{"class":6008},"# Арифметичні операції\n",[5384,12259,12260,12262,12264,12266,12269,12271,12274,12276,12278],{"class":5386,"line":6737},[5384,12261,5523],{"class":5390},[5384,12263,5526],{"class":5509},[5384,12265,5529],{"class":5428},[5384,12267,12268],{"class":5394},"\"a + b: ",[5384,12270,5535],{"class":5428},[5384,12272,12273],{"class":5509},"a + b",[5384,12275,5545],{"class":5428},[5384,12277,5548],{"class":5394},[5384,12279,5551],{"class":5509},[5384,12281,12282,12284,12286,12288,12291,12293,12296,12298,12300],{"class":5386,"line":6760},[5384,12283,5523],{"class":5390},[5384,12285,5526],{"class":5509},[5384,12287,5529],{"class":5428},[5384,12289,12290],{"class":5394},"\"a - b: ",[5384,12292,5535],{"class":5428},[5384,12294,12295],{"class":5509},"a - b",[5384,12297,5545],{"class":5428},[5384,12299,5548],{"class":5394},[5384,12301,5551],{"class":5509},[5384,12303,12304,12306,12308,12310,12313,12315,12318,12320,12322,12324],{"class":5386,"line":7162},[5384,12305,5523],{"class":5390},[5384,12307,5526],{"class":5509},[5384,12309,5529],{"class":5428},[5384,12311,12312],{"class":5394},"\"a * b: ",[5384,12314,5535],{"class":5428},[5384,12316,12317],{"class":5509},"a * b",[5384,12319,5545],{"class":5428},[5384,12321,5548],{"class":5394},[5384,12323,6499],{"class":5509},[5384,12325,12326],{"class":6008},"# поелементне множення\n",[5384,12328,12329,12331,12333,12335,12338,12340,12343,12345,12347],{"class":5386,"line":7172},[5384,12330,5523],{"class":5390},[5384,12332,5526],{"class":5509},[5384,12334,5529],{"class":5428},[5384,12336,12337],{"class":5394},"\"a \u002F b: ",[5384,12339,5535],{"class":5428},[5384,12341,12342],{"class":5509},"a \u002F b",[5384,12344,5545],{"class":5428},[5384,12346,5548],{"class":5394},[5384,12348,5551],{"class":5509},[5384,12350,12351,12353,12355,12357,12360,12362,12365,12367,12369,12371,12373],{"class":5386,"line":9675},[5384,12352,5523],{"class":5390},[5384,12354,5526],{"class":5509},[5384,12356,5529],{"class":5428},[5384,12358,12359],{"class":5394},"\"a ** 2: ",[5384,12361,5535],{"class":5428},[5384,12363,12364],{"class":5509},"a ** ",[5384,12366,6022],{"class":5604},[5384,12368,5545],{"class":5428},[5384,12370,5548],{"class":5394},[5384,12372,6499],{"class":5509},[5384,12374,12375],{"class":6008},"# піднесення до степеня\n",[5615,12377,12378],{"v-slot:output":5379},[5618,12379,12380,12393],{},[5621,12381,12382],{},[5624,12383,12384,12387,12390],{},[5627,12385,12386],{"align":5629},"Арифметична операція",[5627,12388,12389],{"align":5636},"Формула",[5627,12391,12392],{"align":5629},"Отриманий результат",[5639,12394,12395,12410,12425,12440,12455],{},[5624,12396,12397,12402,12405],{},[5644,12398,12399],{"align":5629},[5220,12400,12401],{},"Додавання",[5644,12403,12404],{"align":5636},"$a + b$",[5644,12406,12407],{"align":5629},[5381,12408,12409],{},"tensor([11., 22., 33., 44.])",[5624,12411,12412,12417,12420],{},[5644,12413,12414],{"align":5629},[5220,12415,12416],{},"Віднімання",[5644,12418,12419],{"align":5636},"$a - b$",[5644,12421,12422],{"align":5629},[5381,12423,12424],{},"tensor([ -9., -18., -27., -36.])",[5624,12426,12427,12432,12435],{},[5644,12428,12429],{"align":5629},[5220,12430,12431],{},"Множення Адамара",[5644,12433,12434],{"align":5636},"$a \\odot b$",[5644,12436,12437],{"align":5629},[5381,12438,12439],{},"tensor([ 10.,  40.,  90., 160.])",[5624,12441,12442,12447,12450],{},[5644,12443,12444],{"align":5629},[5220,12445,12446],{},"Ділення",[5644,12448,12449],{"align":5636},"$b \u002F a$",[5644,12451,12452],{"align":5629},[5381,12453,12454],{},"tensor([10., 10., 10., 10.])",[5624,12456,12457,12462,12465],{},[5644,12458,12459],{"align":5629},[5220,12460,12461],{},"Піднесення до степеня",[5644,12463,12464],{"align":5636},"$a^2$",[5644,12466,12467],{"align":5629},[5381,12468,12469],{},"tensor([ 1.,  4.,  9., 16.])",[5987,12471,12472,12612],{"execution-count":6022},[5374,12473,12475],{"className":5496,"code":12474,"language":5498,"meta":5379,"style":5379},"# Математичні функції\nx = torch.tensor([0.0, 1.0, 2.0, 3.0])\n\nprint(f\"exp(x): {torch.exp(x)}\")      # e^x\nprint(f\"log(x+1): {torch.log(x+1)}\")  # натуральний логарифм\nprint(f\"sqrt(x): {torch.sqrt(x)}\")    # квадратний корінь\nprint(f\"sin(x): {torch.sin(x)}\")      # синус\n",[5381,12476,12477,12482,12503,12507,12532,12561,12587],{"__ignoreMap":5379},[5384,12478,12479],{"class":5386,"line":5387},[5384,12480,12481],{"class":6008},"# Математичні функції\n",[5384,12483,12484,12487,12489,12491,12493,12495,12497,12499,12501],{"class":5386,"line":5513},[5384,12485,12486],{"class":5509},"x = torch.tensor([",[5384,12488,7229],{"class":5604},[5384,12490,6019],{"class":5509},[5384,12492,7252],{"class":5604},[5384,12494,6019],{"class":5509},[5384,12496,8059],{"class":5604},[5384,12498,6019],{"class":5509},[5384,12500,8064],{"class":5604},[5384,12502,6039],{"class":5509},[5384,12504,12505],{"class":5386,"line":5520},[5384,12506,5517],{"emptyLinePlaceholder":5516},[5384,12508,12509,12511,12513,12515,12518,12520,12523,12525,12527,12529],{"class":5386,"line":5554},[5384,12510,5523],{"class":5390},[5384,12512,5526],{"class":5509},[5384,12514,5529],{"class":5428},[5384,12516,12517],{"class":5394},"\"exp(x): ",[5384,12519,5535],{"class":5428},[5384,12521,12522],{"class":5509},"torch.exp(x)",[5384,12524,5545],{"class":5428},[5384,12526,5548],{"class":5394},[5384,12528,8815],{"class":5509},[5384,12530,12531],{"class":6008},"# e^x\n",[5384,12533,12534,12536,12538,12540,12543,12545,12548,12550,12552,12554,12556,12558],{"class":5386,"line":5577},[5384,12535,5523],{"class":5390},[5384,12537,5526],{"class":5509},[5384,12539,5529],{"class":5428},[5384,12541,12542],{"class":5394},"\"log(x+1): ",[5384,12544,5535],{"class":5428},[5384,12546,12547],{"class":5509},"torch.log(x+",[5384,12549,5493],{"class":5604},[5384,12551,5227],{"class":5509},[5384,12553,5545],{"class":5428},[5384,12555,5548],{"class":5394},[5384,12557,6499],{"class":5509},[5384,12559,12560],{"class":6008},"# натуральний логарифм\n",[5384,12562,12563,12565,12567,12569,12572,12574,12577,12579,12581,12584],{"class":5386,"line":5586},[5384,12564,5523],{"class":5390},[5384,12566,5526],{"class":5509},[5384,12568,5529],{"class":5428},[5384,12570,12571],{"class":5394},"\"sqrt(x): ",[5384,12573,5535],{"class":5428},[5384,12575,12576],{"class":5509},"torch.sqrt(x)",[5384,12578,5545],{"class":5428},[5384,12580,5548],{"class":5394},[5384,12582,12583],{"class":5509},")    ",[5384,12585,12586],{"class":6008},"# квадратний корінь\n",[5384,12588,12589,12591,12593,12595,12598,12600,12603,12605,12607,12609],{"class":5386,"line":6086},[5384,12590,5523],{"class":5390},[5384,12592,5526],{"class":5509},[5384,12594,5529],{"class":5428},[5384,12596,12597],{"class":5394},"\"sin(x): ",[5384,12599,5535],{"class":5428},[5384,12601,12602],{"class":5509},"torch.sin(x)",[5384,12604,5545],{"class":5428},[5384,12606,5548],{"class":5394},[5384,12608,8815],{"class":5509},[5384,12610,12611],{"class":6008},"# синус\n",[5615,12613,12614],{"v-slot:output":5379},[5618,12615,12616,12629],{},[5621,12617,12618],{},[5624,12619,12620,12623,12626],{},[5627,12621,12622],{"align":5629},"Математична функція",[5627,12624,12625],{"align":5636},"Вхідний вектор $x$",[5627,12627,12628],{"align":5629},"Обчислений результат",[5639,12630,12631,12648,12664,12680],{},[5624,12632,12633,12638,12643],{},[5644,12634,12635],{"align":5629},[5220,12636,12637],{},"Експонента $\\exp(x)$",[5644,12639,12640],{"align":5636},[5381,12641,12642],{},"[0., 1., 2., 3.]",[5644,12644,12645],{"align":5629},[5381,12646,12647],{},"tensor([ 1.0000,  2.7183,  7.3891, 20.0855])",[5624,12649,12650,12655,12659],{},[5644,12651,12652],{"align":5629},[5220,12653,12654],{},"Логарифм $\\ln(x + 1)$",[5644,12656,12657],{"align":5636},[5381,12658,12642],{},[5644,12660,12661],{"align":5629},[5381,12662,12663],{},"tensor([0.0000, 0.6931, 1.0986, 1.3863])",[5624,12665,12666,12671,12675],{},[5644,12667,12668],{"align":5629},[5220,12669,12670],{},"Квадратний корінь $\\sqrt{x}$",[5644,12672,12673],{"align":5636},[5381,12674,12642],{},[5644,12676,12677],{"align":5629},[5381,12678,12679],{},"tensor([0.0000, 1.0000, 1.4142, 1.7321])",[5624,12681,12682,12687,12691],{},[5644,12683,12684],{"align":5629},[5220,12685,12686],{},"Синус $\\sin(x)$",[5644,12688,12689],{"align":5636},[5381,12690,12642],{},[5644,12692,12693],{"align":5629},[5381,12694,12695],{},"tensor([0.0000, 0.8415, 0.9093, 0.1411])",[12129,12697,12699],{"id":12698},"матричні-операції","Матричні операції",[5262,12701,12702],{},"Для нейронних мереж критично важливі операції лінійної алгебри:",[5490,12704,12706],{"title":12705},"matrix_ops.ipynb",[5987,12707,12708,12976],{"execution-count":5493},[5374,12709,12711],{"className":5496,"code":12710,"language":5498,"meta":5379,"style":5379},"import torch\n\nA = torch.tensor([\n    [1, 2],\n    [3, 4]\n])\nB = torch.tensor([\n    [5, 6],\n    [7, 8]\n])\n\nprint(f\"Матриця A:\\n{A}\\n\")\nprint(f\"Матриця B:\\n{B}\\n\")\n\n# Поелементне множення (НЕ матричне!)\nprint(f\"A * B (поелементне):\\n{A * B}\\n\")\n\n# Матричне множення (правильне)\nprint(f\"A @ B (матричне):\\n{A @ B}\\n\")\n\n# Альтернативні способи матричного множення\nprint(f\"torch.matmul(A, B):\\n{torch.matmul(A, B)}\\n\")\nprint(f\"torch.mm(A, B):\\n{torch.mm(A, B)}\")\n",[5381,12712,12713,12719,12723,12728,12740,12752,12756,12761,12773,12785,12789,12793,12819,12845,12849,12854,12880,12884,12889,12915,12919,12924,12951],{"__ignoreMap":5379},[5384,12714,12715,12717],{"class":5386,"line":5387},[5384,12716,5506],{"class":5505},[5384,12718,5510],{"class":5509},[5384,12720,12721],{"class":5386,"line":5513},[5384,12722,5517],{"emptyLinePlaceholder":5516},[5384,12724,12725],{"class":5386,"line":5520},[5384,12726,12727],{"class":5509},"A = torch.tensor([\n",[5384,12729,12730,12732,12734,12736,12738],{"class":5386,"line":5554},[5384,12731,6188],{"class":5509},[5384,12733,5493],{"class":5604},[5384,12735,6019],{"class":5509},[5384,12737,6022],{"class":5604},[5384,12739,6201],{"class":5509},[5384,12741,12742,12744,12746,12748,12750],{"class":5386,"line":5577},[5384,12743,6188],{"class":5509},[5384,12745,5743],{"class":5604},[5384,12747,6019],{"class":5509},[5384,12749,6031],{"class":5604},[5384,12751,6219],{"class":5509},[5384,12753,12754],{"class":5386,"line":5586},[5384,12755,6039],{"class":5509},[5384,12757,12758],{"class":5386,"line":6086},[5384,12759,12760],{"class":5509},"B = torch.tensor([\n",[5384,12762,12763,12765,12767,12769,12771],{"class":5386,"line":6273},[5384,12764,6188],{"class":5509},[5384,12766,6036],{"class":5604},[5384,12768,6019],{"class":5509},[5384,12770,6216],{"class":5604},[5384,12772,6201],{"class":5509},[5384,12774,12775,12777,12779,12781,12783],{"class":5386,"line":6713},[5384,12776,6188],{"class":5509},[5384,12778,7292],{"class":5604},[5384,12780,6019],{"class":5509},[5384,12782,10316],{"class":5604},[5384,12784,6219],{"class":5509},[5384,12786,12787],{"class":5386,"line":6737},[5384,12788,6039],{"class":5509},[5384,12790,12791],{"class":5386,"line":6760},[5384,12792,5517],{"emptyLinePlaceholder":5516},[5384,12794,12795,12797,12799,12801,12804,12806,12808,12811,12813,12815,12817],{"class":5386,"line":7162},[5384,12796,5523],{"class":5390},[5384,12798,5526],{"class":5509},[5384,12800,5529],{"class":5428},[5384,12802,12803],{"class":5394},"\"Матриця A:",[5384,12805,6238],{"class":6237},[5384,12807,5535],{"class":5428},[5384,12809,12810],{"class":5509},"A",[5384,12812,5545],{"class":5428},[5384,12814,6238],{"class":6237},[5384,12816,5548],{"class":5394},[5384,12818,5551],{"class":5509},[5384,12820,12821,12823,12825,12827,12830,12832,12834,12837,12839,12841,12843],{"class":5386,"line":7172},[5384,12822,5523],{"class":5390},[5384,12824,5526],{"class":5509},[5384,12826,5529],{"class":5428},[5384,12828,12829],{"class":5394},"\"Матриця B:",[5384,12831,6238],{"class":6237},[5384,12833,5535],{"class":5428},[5384,12835,12836],{"class":5509},"B",[5384,12838,5545],{"class":5428},[5384,12840,6238],{"class":6237},[5384,12842,5548],{"class":5394},[5384,12844,5551],{"class":5509},[5384,12846,12847],{"class":5386,"line":9675},[5384,12848,5517],{"emptyLinePlaceholder":5516},[5384,12850,12851],{"class":5386,"line":9681},[5384,12852,12853],{"class":6008},"# Поелементне множення (НЕ матричне!)\n",[5384,12855,12856,12858,12860,12862,12865,12867,12869,12872,12874,12876,12878],{"class":5386,"line":9687},[5384,12857,5523],{"class":5390},[5384,12859,5526],{"class":5509},[5384,12861,5529],{"class":5428},[5384,12863,12864],{"class":5394},"\"A * B (поелементне):",[5384,12866,6238],{"class":6237},[5384,12868,5535],{"class":5428},[5384,12870,12871],{"class":5509},"A * B",[5384,12873,5545],{"class":5428},[5384,12875,6238],{"class":6237},[5384,12877,5548],{"class":5394},[5384,12879,5551],{"class":5509},[5384,12881,12882],{"class":5386,"line":9710},[5384,12883,5517],{"emptyLinePlaceholder":5516},[5384,12885,12886],{"class":5386,"line":9732},[5384,12887,12888],{"class":6008},"# Матричне множення (правильне)\n",[5384,12890,12891,12893,12895,12897,12900,12902,12904,12907,12909,12911,12913],{"class":5386,"line":10489},[5384,12892,5523],{"class":5390},[5384,12894,5526],{"class":5509},[5384,12896,5529],{"class":5428},[5384,12898,12899],{"class":5394},"\"A @ B (матричне):",[5384,12901,6238],{"class":6237},[5384,12903,5535],{"class":5428},[5384,12905,12906],{"class":5509},"A @ B",[5384,12908,5545],{"class":5428},[5384,12910,6238],{"class":6237},[5384,12912,5548],{"class":5394},[5384,12914,5551],{"class":5509},[5384,12916,12917],{"class":5386,"line":10494},[5384,12918,5517],{"emptyLinePlaceholder":5516},[5384,12920,12921],{"class":5386,"line":10500},[5384,12922,12923],{"class":6008},"# Альтернативні способи матричного множення\n",[5384,12925,12927,12929,12931,12933,12936,12938,12940,12943,12945,12947,12949],{"class":5386,"line":12926},22,[5384,12928,5523],{"class":5390},[5384,12930,5526],{"class":5509},[5384,12932,5529],{"class":5428},[5384,12934,12935],{"class":5394},"\"torch.matmul(A, B):",[5384,12937,6238],{"class":6237},[5384,12939,5535],{"class":5428},[5384,12941,12942],{"class":5509},"torch.matmul(A, B)",[5384,12944,5545],{"class":5428},[5384,12946,6238],{"class":6237},[5384,12948,5548],{"class":5394},[5384,12950,5551],{"class":5509},[5384,12952,12954,12956,12958,12960,12963,12965,12967,12970,12972,12974],{"class":5386,"line":12953},23,[5384,12955,5523],{"class":5390},[5384,12957,5526],{"class":5509},[5384,12959,5529],{"class":5428},[5384,12961,12962],{"class":5394},"\"torch.mm(A, B):",[5384,12964,6238],{"class":6237},[5384,12966,5535],{"class":5428},[5384,12968,12969],{"class":5509},"torch.mm(A, B)",[5384,12971,5545],{"class":5428},[5384,12973,5548],{"class":5394},[5384,12975,5551],{"class":5509},[5615,12977,12978],{"v-slot:output":5379},[5618,12979,12980,12995],{},[5621,12981,12982],{},[5624,12983,12984,12987,12990,12993],{},[5627,12985,12986],{"align":5629},"Тип множення",[5627,12988,12989],{"align":5636},"Оператор \u002F Функція",[5627,12991,12992],{"align":5636},"Розмірності",[5627,12994,10146],{"align":5629},[5639,12996,12997,13018],{},[5624,12998,12999,13004,13008,13013],{},[5644,13000,13001],{"align":5629},[5220,13002,13003],{},"Поелементне",[5644,13005,13006],{"align":5636},[5381,13007,12871],{},[5644,13009,13010],{"align":5636},[5381,13011,13012],{},"(2,2) * (2,2)",[5644,13014,13015],{"align":5629},[5381,13016,13017],{},"tensor([[ 5, 12], [21, 32]])",[5624,13019,13020,13025,13032,13037],{},[5644,13021,13022],{"align":5629},[5220,13023,13024],{},"Матричне (Dot product)",[5644,13026,13027,7027,13029],{"align":5636},[5381,13028,12906],{},[5381,13030,13031],{},"torch.matmul",[5644,13033,13034],{"align":5636},[5381,13035,13036],{},"(2,2) @ (2,2)",[5644,13038,13039],{"align":5629},[5381,13040,13041],{},"tensor([[19, 22], [43, 50]])",[7010,13043,13044,13063],{},[5262,13045,13046,13048,13049,13052,13053,13056,13057,7027,13060,7031],{},[5220,13047,7014],{}," Оператор ",[5381,13050,13051],{},"*"," у PyTorch виконує ",[5220,13054,13055],{},"поелементне"," множення, а не матричне! Для матричного множення використовуйте ",[5381,13058,13059],{},"@",[5381,13061,13062],{},"torch.matmul()",[5262,13064,13065,13066,13068,13069,13071],{},"Це відрізняється від NumPy, де ",[5381,13067,13051],{}," також виконує поелементне множення, але ",[5381,13070,13059],{}," з'явився лише у Python 3.5+.",[5262,13073,13074],{},[5268,13075],{"alt":13076,"className":13077,"src":13078},"Порівняння поелементного множення Адамара та матричного скалярного добутку matmul",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F06.png",[5275,13080,13082,13084,13087,13090],{"alt":13081},"Порівняння матричного множення (matmul) та поелементного (Hadamard)",[5262,13083,5280],{},[5262,13085,13086],{},"rectangle \"Поелементне множення: A * B\" as elem #EFF6FF {\ncard \"Кожен елемент cᵢⱼ = aᵢⱼ · bᵢⱼ\\nВимога: ідентичні форми матриць\" as e_desc\n}",[5262,13088,13089],{},"rectangle \"Матричне множення: A @ B (torch.matmul)\" as matmul #ECFDF5 {\ncard \"Скалярний добуток рядка A на стовпець B:\\ncᵢⱼ = Σₖ aᵢₖ · bₖⱼ\\nВимога: внутрішні розмірності (m x k) @ (k x n) = (m x n)\" as m_desc\n}",[5262,13091,5292],{},[12129,13093,13095],{"id":13094},"агрегатні-операції","Агрегатні операції",[5262,13097,13098],{},"Обчислення статистик по тензорам:",[5490,13100,13102,13392,13576],{"title":13101},"aggregation.ipynb",[5987,13103,13104,13306],{"execution-count":5493},[5374,13105,13107],{"className":5496,"code":13106,"language":5498,"meta":5379,"style":5379},"import torch\n\nmatrix = torch.tensor([\n    [1.0, 2.0, 3.0],\n    [4.0, 5.0, 6.0]\n])\n\nprint(f\"Матриця:\\n{matrix}\\n\")\n\n# Операції по всьому тензору\nprint(f\"Сума всіх елементів: {matrix.sum()}\")\nprint(f\"Середнє всіх елементів: {matrix.mean()}\")\nprint(f\"Максимум: {matrix.max()}\")\nprint(f\"Мінімум: {matrix.min()}\")\nprint(f\"Стандартне відхилення: {matrix.std()}\")\n",[5381,13108,13109,13115,13119,13123,13139,13155,13159,13163,13187,13191,13196,13218,13240,13262,13284],{"__ignoreMap":5379},[5384,13110,13111,13113],{"class":5386,"line":5387},[5384,13112,5506],{"class":5505},[5384,13114,5510],{"class":5509},[5384,13116,13117],{"class":5386,"line":5513},[5384,13118,5517],{"emptyLinePlaceholder":5516},[5384,13120,13121],{"class":5386,"line":5520},[5384,13122,6183],{"class":5509},[5384,13124,13125,13127,13129,13131,13133,13135,13137],{"class":5386,"line":5554},[5384,13126,6188],{"class":5509},[5384,13128,7252],{"class":5604},[5384,13130,6019],{"class":5509},[5384,13132,8059],{"class":5604},[5384,13134,6019],{"class":5509},[5384,13136,8064],{"class":5604},[5384,13138,6201],{"class":5509},[5384,13140,13141,13143,13145,13147,13149,13151,13153],{"class":5386,"line":5577},[5384,13142,6188],{"class":5509},[5384,13144,8445],{"class":5604},[5384,13146,6019],{"class":5509},[5384,13148,8450],{"class":5604},[5384,13150,6019],{"class":5509},[5384,13152,8455],{"class":5604},[5384,13154,6219],{"class":5509},[5384,13156,13157],{"class":5386,"line":5586},[5384,13158,6039],{"class":5509},[5384,13160,13161],{"class":5386,"line":6086},[5384,13162,5517],{"emptyLinePlaceholder":5516},[5384,13164,13165,13167,13169,13171,13173,13175,13177,13179,13181,13183,13185],{"class":5386,"line":6273},[5384,13166,5523],{"class":5390},[5384,13168,5526],{"class":5509},[5384,13170,5529],{"class":5428},[5384,13172,6234],{"class":5394},[5384,13174,6238],{"class":6237},[5384,13176,5535],{"class":5428},[5384,13178,6243],{"class":5509},[5384,13180,5545],{"class":5428},[5384,13182,6238],{"class":6237},[5384,13184,5548],{"class":5394},[5384,13186,5551],{"class":5509},[5384,13188,13189],{"class":5386,"line":6713},[5384,13190,5517],{"emptyLinePlaceholder":5516},[5384,13192,13193],{"class":5386,"line":6737},[5384,13194,13195],{"class":6008},"# Операції по всьому тензору\n",[5384,13197,13198,13200,13202,13204,13207,13209,13212,13214,13216],{"class":5386,"line":6760},[5384,13199,5523],{"class":5390},[5384,13201,5526],{"class":5509},[5384,13203,5529],{"class":5428},[5384,13205,13206],{"class":5394},"\"Сума всіх елементів: ",[5384,13208,5535],{"class":5428},[5384,13210,13211],{"class":5509},"matrix.sum()",[5384,13213,5545],{"class":5428},[5384,13215,5548],{"class":5394},[5384,13217,5551],{"class":5509},[5384,13219,13220,13222,13224,13226,13229,13231,13234,13236,13238],{"class":5386,"line":7162},[5384,13221,5523],{"class":5390},[5384,13223,5526],{"class":5509},[5384,13225,5529],{"class":5428},[5384,13227,13228],{"class":5394},"\"Середнє всіх елементів: ",[5384,13230,5535],{"class":5428},[5384,13232,13233],{"class":5509},"matrix.mean()",[5384,13235,5545],{"class":5428},[5384,13237,5548],{"class":5394},[5384,13239,5551],{"class":5509},[5384,13241,13242,13244,13246,13248,13251,13253,13256,13258,13260],{"class":5386,"line":7172},[5384,13243,5523],{"class":5390},[5384,13245,5526],{"class":5509},[5384,13247,5529],{"class":5428},[5384,13249,13250],{"class":5394},"\"Максимум: ",[5384,13252,5535],{"class":5428},[5384,13254,13255],{"class":5509},"matrix.max()",[5384,13257,5545],{"class":5428},[5384,13259,5548],{"class":5394},[5384,13261,5551],{"class":5509},[5384,13263,13264,13266,13268,13270,13273,13275,13278,13280,13282],{"class":5386,"line":9675},[5384,13265,5523],{"class":5390},[5384,13267,5526],{"class":5509},[5384,13269,5529],{"class":5428},[5384,13271,13272],{"class":5394},"\"Мінімум: ",[5384,13274,5535],{"class":5428},[5384,13276,13277],{"class":5509},"matrix.min()",[5384,13279,5545],{"class":5428},[5384,13281,5548],{"class":5394},[5384,13283,5551],{"class":5509},[5384,13285,13286,13288,13290,13292,13295,13297,13300,13302,13304],{"class":5386,"line":9681},[5384,13287,5523],{"class":5390},[5384,13289,5526],{"class":5509},[5384,13291,5529],{"class":5428},[5384,13293,13294],{"class":5394},"\"Стандартне відхилення: ",[5384,13296,5535],{"class":5428},[5384,13298,13299],{"class":5509},"matrix.std()",[5384,13301,5545],{"class":5428},[5384,13303,5548],{"class":5394},[5384,13305,5551],{"class":5509},[5615,13307,13308],{"v-slot:output":5379},[5618,13309,13310,13323],{},[5621,13311,13312],{},[5624,13313,13314,13317,13320],{},[5627,13315,13316],{"align":5629},"Статистична метрика",[5627,13318,13319],{"align":5629},"PyTorch метод",[5627,13321,13322],{"align":5636},"Обчислене значення",[5639,13324,13325,13341,13356,13372],{},[5624,13326,13327,13332,13336],{},[5644,13328,13329],{"align":5629},[5220,13330,13331],{},"Загальна сума",[5644,13333,13334],{"align":5629},[5381,13335,13211],{},[5644,13337,13338],{"align":5636},[5381,13339,13340],{},"21.0",[5624,13342,13343,13348,13352],{},[5644,13344,13345],{"align":5629},[5220,13346,13347],{},"Середнє арифметичне",[5644,13349,13350],{"align":5629},[5381,13351,13233],{},[5644,13353,13354],{"align":5636},[5381,13355,6881],{},[5624,13357,13358,13363,13367],{},[5644,13359,13360],{"align":5629},[5220,13361,13362],{},"Стандартне відхилення",[5644,13364,13365],{"align":5629},[5381,13366,13299],{},[5644,13368,13369],{"align":5636},[5381,13370,13371],{},"1.8708",[5624,13373,13374,13379,13385],{},[5644,13375,13376],{"align":5629},[5220,13377,13378],{},"Мінімум \u002F Максимум",[5644,13380,13381,6019,13383],{"align":5629},[5381,13382,13277],{},[5381,13384,13255],{},[5644,13386,13387,13389,13390],{"align":5636},[5381,13388,7252],{}," \u002F ",[5381,13391,8455],{},[5987,13393,13394,13487],{"execution-count":6022},[5374,13395,13397],{"className":5496,"code":13396,"language":5498,"meta":5379,"style":5379},"# Операції по конкретній вісі\nprint(\"Сума по рядках (вісь 0):\", matrix.sum(dim=0))\nprint(\"Сума по стовпцях (вісь 1):\", matrix.sum(dim=1))\nprint(\"\\nСереднє по рядках:\", matrix.mean(dim=0))\nprint(\"Середнє по стовпцях:\", matrix.mean(dim=1))\n",[5381,13398,13399,13404,13425,13444,13468],{"__ignoreMap":5379},[5384,13400,13401],{"class":5386,"line":5387},[5384,13402,13403],{"class":6008},"# Операції по конкретній вісі\n",[5384,13405,13406,13408,13410,13413,13416,13419,13421,13423],{"class":5386,"line":5513},[5384,13407,5523],{"class":5390},[5384,13409,5526],{"class":5509},[5384,13411,13412],{"class":5394},"\"Сума по рядках (вісь 0):\"",[5384,13414,13415],{"class":5509},", matrix.sum(",[5384,13417,13418],{"class":5541},"dim",[5384,13420,7635],{"class":5509},[5384,13422,5605],{"class":5604},[5384,13424,7663],{"class":5509},[5384,13426,13427,13429,13431,13434,13436,13438,13440,13442],{"class":5386,"line":5520},[5384,13428,5523],{"class":5390},[5384,13430,5526],{"class":5509},[5384,13432,13433],{"class":5394},"\"Сума по стовпцях (вісь 1):\"",[5384,13435,13415],{"class":5509},[5384,13437,13418],{"class":5541},[5384,13439,7635],{"class":5509},[5384,13441,5493],{"class":5604},[5384,13443,7663],{"class":5509},[5384,13445,13446,13448,13450,13452,13454,13457,13460,13462,13464,13466],{"class":5386,"line":5554},[5384,13447,5523],{"class":5390},[5384,13449,5526],{"class":5509},[5384,13451,5548],{"class":5394},[5384,13453,6238],{"class":6237},[5384,13455,13456],{"class":5394},"Середнє по рядках:\"",[5384,13458,13459],{"class":5509},", matrix.mean(",[5384,13461,13418],{"class":5541},[5384,13463,7635],{"class":5509},[5384,13465,5605],{"class":5604},[5384,13467,7663],{"class":5509},[5384,13469,13470,13472,13474,13477,13479,13481,13483,13485],{"class":5386,"line":5577},[5384,13471,5523],{"class":5390},[5384,13473,5526],{"class":5509},[5384,13475,13476],{"class":5394},"\"Середнє по стовпцях:\"",[5384,13478,13459],{"class":5509},[5384,13480,13418],{"class":5541},[5384,13482,7635],{"class":5509},[5384,13484,5493],{"class":5604},[5384,13486,7663],{"class":5509},[5615,13488,13489],{"v-slot:output":5379},[5618,13490,13491,13506],{},[5621,13492,13493],{},[5624,13494,13495,13498,13501,13503],{},[5627,13496,13497],{"align":5629},"Вісь агрегації",[5627,13499,13500],{"align":5629},"PyTorch виклик",[5627,13502,7208],{"align":5629},[5627,13504,13505],{"align":5636},"Форма результату",[5639,13507,13508,13529,13551],{},[5624,13509,13510,13515,13520,13525],{},[5644,13511,13512],{"align":5629},[5220,13513,13514],{},"По стовпчиках (схлопування рядків)",[5644,13516,13517],{"align":5629},[5381,13518,13519],{},"matrix.sum(dim=0)",[5644,13521,13522],{"align":5629},[5381,13523,13524],{},"tensor([5., 7., 9.])",[5644,13526,13527],{"align":5636},[5381,13528,10613],{},[5624,13530,13531,13536,13541,13546],{},[5644,13532,13533],{"align":5629},[5220,13534,13535],{},"По рядках (схлопування стовпчиків)",[5644,13537,13538],{"align":5629},[5381,13539,13540],{},"matrix.sum(dim=1)",[5644,13542,13543],{"align":5629},[5381,13544,13545],{},"tensor([ 6., 15.])",[5644,13547,13548],{"align":5636},[5381,13549,13550],{},"torch.Size([2])",[5624,13552,13553,13561,13566,13571],{},[5644,13554,13555],{"align":5629},[5220,13556,13557,13558,5227],{},"Зі збереженням виміру (",[5381,13559,13560],{},"keepdim=True",[5644,13562,13563],{"align":5629},[5381,13564,13565],{},"matrix.sum(dim=1, keepdim=True)",[5644,13567,13568],{"align":5629},[5381,13569,13570],{},"tensor([[6.], [15.]])",[5644,13572,13573],{"align":5636},[5381,13574,13575],{},"torch.Size([2, 1])",[5987,13577,13578,13695],{"execution-count":5743},[5374,13579,13581],{"className":5496,"code":13580,"language":5498,"meta":5379,"style":5379},"# argmax та argmin — повертають ІНДЕКСИ екстремумів\nvalues = torch.tensor([3.2, 1.7, 5.9, 2.4, 4.1])\n\nprint(f\"Значення: {values}\")\nprint(f\"Індекс максимуму: {values.argmax()}\")   # 2 (бо values[2] = 5.9)\nprint(f\"Індекс мінімуму: {values.argmin()}\")     # 1 (бо values[1] = 1.7)\n",[5381,13582,13583,13588,13618,13622,13644,13670],{"__ignoreMap":5379},[5384,13584,13585],{"class":5386,"line":5387},[5384,13586,13587],{"class":6008},"# argmax та argmin — повертають ІНДЕКСИ екстремумів\n",[5384,13589,13590,13593,13596,13598,13601,13603,13606,13608,13611,13613,13616],{"class":5386,"line":5513},[5384,13591,13592],{"class":5509},"values = torch.tensor([",[5384,13594,13595],{"class":5604},"3.2",[5384,13597,6019],{"class":5509},[5384,13599,13600],{"class":5604},"1.7",[5384,13602,6019],{"class":5509},[5384,13604,13605],{"class":5604},"5.9",[5384,13607,6019],{"class":5509},[5384,13609,13610],{"class":5604},"2.4",[5384,13612,6019],{"class":5509},[5384,13614,13615],{"class":5604},"4.1",[5384,13617,6039],{"class":5509},[5384,13619,13620],{"class":5386,"line":5520},[5384,13621,5517],{"emptyLinePlaceholder":5516},[5384,13623,13624,13626,13628,13630,13633,13635,13638,13640,13642],{"class":5386,"line":5554},[5384,13625,5523],{"class":5390},[5384,13627,5526],{"class":5509},[5384,13629,5529],{"class":5428},[5384,13631,13632],{"class":5394},"\"Значення: ",[5384,13634,5535],{"class":5428},[5384,13636,13637],{"class":5509},"values",[5384,13639,5545],{"class":5428},[5384,13641,5548],{"class":5394},[5384,13643,5551],{"class":5509},[5384,13645,13646,13648,13650,13652,13655,13657,13660,13662,13664,13667],{"class":5386,"line":5577},[5384,13647,5523],{"class":5390},[5384,13649,5526],{"class":5509},[5384,13651,5529],{"class":5428},[5384,13653,13654],{"class":5394},"\"Індекс максимуму: ",[5384,13656,5535],{"class":5428},[5384,13658,13659],{"class":5509},"values.argmax()",[5384,13661,5545],{"class":5428},[5384,13663,5548],{"class":5394},[5384,13665,13666],{"class":5509},")   ",[5384,13668,13669],{"class":6008},"# 2 (бо values[2] = 5.9)\n",[5384,13671,13672,13674,13676,13678,13681,13683,13686,13688,13690,13692],{"class":5386,"line":5586},[5384,13673,5523],{"class":5390},[5384,13675,5526],{"class":5509},[5384,13677,5529],{"class":5428},[5384,13679,13680],{"class":5394},"\"Індекс мінімуму: ",[5384,13682,5535],{"class":5428},[5384,13684,13685],{"class":5509},"values.argmin()",[5384,13687,5545],{"class":5428},[5384,13689,5548],{"class":5394},[5384,13691,8841],{"class":5509},[5384,13693,13694],{"class":6008},"# 1 (бо values[1] = 1.7)\n",[5615,13696,13697],{"v-slot:output":5379},[5618,13698,13699,13715],{},[5621,13700,13701],{},[5624,13702,13703,13706,13709,13712],{},[5627,13704,13705],{"align":5629},"Показник",[5627,13707,13708],{"align":5636},"Значення елемента",[5627,13710,13711],{"align":5636},"Знайдений індекс",[5627,13713,13714],{"align":5629},"PyTorch функція",[5639,13716,13717,13740],{},[5624,13718,13719,13724,13729,13735],{},[5644,13720,13721],{"align":5629},[5220,13722,13723],{},"Максимум",[5644,13725,13726],{"align":5636},[5381,13727,13728],{},"5.9000",[5644,13730,13731],{"align":5636},[5220,13732,13733],{},[5381,13734,6022],{},[5644,13736,13737],{"align":5629},[5381,13738,13739],{},"torch.argmax(x)",[5624,13741,13742,13747,13752,13758],{},[5644,13743,13744],{"align":5629},[5220,13745,13746],{},"Мінімальний елемент",[5644,13748,13749],{"align":5636},[5381,13750,13751],{},"1.7000",[5644,13753,13754],{"align":5636},[5220,13755,13756],{},[5381,13757,5493],{},[5644,13759,13760],{"align":5629},[5381,13761,13762],{},"torch.argmin(x)",[5351,13764,13765],{},[5262,13766,13767,13774,13775,13778],{},[5220,13768,13769,13770,13773],{},"Що означає вісь (",[5224,13771,13772],{},"dimension",")?"," У двовимірному тензорі вісь 0 — це рядки (вертикаль), вісь 1 — стовпці (горизонталь). Операція ",[5381,13776,13777],{},"sum(dim=0)"," означає \"просумуй по рядках\" і поверне вектор довжини, рівної кількості стовпців.",[5695,13780,13782],{"id":13781},"практичні-кейси-з-тензорами","Практичні кейси з тензорами",[5262,13784,13785],{},"Розглянемо реальні задачі обробки даних, які виникають при підготовці датасетів для нейронних мереж.",[5979,13787,13789],{"id":13788},"кейс-1-нормалізація-зображення","Кейс 1: Нормалізація зображення",[5262,13791,13792,13793,7027,13796,9443],{},"Зображення зазвичай представлені пікселями зі значеннями від 0 до 255. Для нейронних мереж краще нормалізувати їх до діапазону ",[5381,13794,13795],{},"[-1, 1]",[5381,13797,13798],{},"[0, 1]",[5490,13800,13802],{"title":13801},"image_normalization.ipynb",[5987,13803,13804,14117],{"execution-count":5493},[5374,13805,13807],{"className":5496,"code":13806,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Імітуємо зображення 2×2 пікселі, RGB (3 канали)\n# Форма: (height, width, channels)\nimage = torch.tensor([\n    [[255, 0, 128], [200, 50, 100]],\n    [[150, 200, 75], [100, 100, 100]]\n], dtype=torch.float32)\n\nprint(f\"Оригінальне зображення [0, 255]:\\n{image}\\n\")\nprint(f\"Форма: {image.shape}\")\nprint(f\"Мін: {image.min()}, Макс: {image.max()}\\n\")\n\n# Нормалізація до [0, 1]\nnormalized_01 = image \u002F 255.0\nprint(f\"Нормалізація [0, 1]:\\n{normalized_01}\\n\")\n\n# Нормалізація до [-1, 1]\nnormalized_11 = (image \u002F 255.0) * 2.0 - 1.0\nprint(f\"Нормалізація [-1, 1]:\\n{normalized_11}\\n\")\nprint(f\"Мін: {normalized_11.min():.4f}, Макс: {normalized_11.max():.4f}\")\n",[5381,13808,13809,13815,13819,13824,13829,13833,13866,13897,13906,13910,13935,13955,13989,13993,13998,14006,14032,14036,14041,14060,14086],{"__ignoreMap":5379},[5384,13810,13811,13813],{"class":5386,"line":5387},[5384,13812,5506],{"class":5505},[5384,13814,5510],{"class":5509},[5384,13816,13817],{"class":5386,"line":5513},[5384,13818,5517],{"emptyLinePlaceholder":5516},[5384,13820,13821],{"class":5386,"line":5520},[5384,13822,13823],{"class":6008},"# Імітуємо зображення 2×2 пікселі, RGB (3 канали)\n",[5384,13825,13826],{"class":5386,"line":5554},[5384,13827,13828],{"class":6008},"# Форма: (height, width, channels)\n",[5384,13830,13831],{"class":5386,"line":5577},[5384,13832,6382],{"class":5509},[5384,13834,13835,13837,13839,13841,13843,13845,13848,13850,13853,13855,13858,13860,13863],{"class":5386,"line":5586},[5384,13836,6387],{"class":5509},[5384,13838,6390],{"class":5604},[5384,13840,6019],{"class":5509},[5384,13842,5605],{"class":5604},[5384,13844,6019],{"class":5509},[5384,13846,13847],{"class":5604},"128",[5384,13849,6401],{"class":5509},[5384,13851,13852],{"class":5604},"200",[5384,13854,6019],{"class":5509},[5384,13856,13857],{"class":5604},"50",[5384,13859,6019],{"class":5509},[5384,13861,13862],{"class":5604},"100",[5384,13864,13865],{"class":5509},"]],\n",[5384,13867,13868,13870,13873,13875,13877,13879,13882,13884,13886,13888,13890,13892,13894],{"class":5386,"line":6086},[5384,13869,6387],{"class":5509},[5384,13871,13872],{"class":5604},"150",[5384,13874,6019],{"class":5509},[5384,13876,13852],{"class":5604},[5384,13878,6019],{"class":5509},[5384,13880,13881],{"class":5604},"75",[5384,13883,6401],{"class":5509},[5384,13885,13862],{"class":5604},[5384,13887,6019],{"class":5509},[5384,13889,13862],{"class":5604},[5384,13891,6019],{"class":5509},[5384,13893,13862],{"class":5604},[5384,13895,13896],{"class":5509},"]]\n",[5384,13898,13899,13901,13903],{"class":5386,"line":6273},[5384,13900,8139],{"class":5509},[5384,13902,6138],{"class":5541},[5384,13904,13905],{"class":5509},"=torch.float32)\n",[5384,13907,13908],{"class":5386,"line":6713},[5384,13909,5517],{"emptyLinePlaceholder":5516},[5384,13911,13912,13914,13916,13918,13921,13923,13925,13927,13929,13931,13933],{"class":5386,"line":6737},[5384,13913,5523],{"class":5390},[5384,13915,5526],{"class":5509},[5384,13917,5529],{"class":5428},[5384,13919,13920],{"class":5394},"\"Оригінальне зображення [0, 255]:",[5384,13922,6238],{"class":6237},[5384,13924,5535],{"class":5428},[5384,13926,6471],{"class":5509},[5384,13928,5545],{"class":5428},[5384,13930,6238],{"class":6237},[5384,13932,5548],{"class":5394},[5384,13934,5551],{"class":5509},[5384,13936,13937,13939,13941,13943,13945,13947,13949,13951,13953],{"class":5386,"line":6760},[5384,13938,5523],{"class":5390},[5384,13940,5526],{"class":5509},[5384,13942,5529],{"class":5428},[5384,13944,6072],{"class":5394},[5384,13946,5535],{"class":5428},[5384,13948,6492],{"class":5509},[5384,13950,5545],{"class":5428},[5384,13952,5548],{"class":5394},[5384,13954,5551],{"class":5509},[5384,13956,13957,13959,13961,13963,13966,13968,13971,13973,13976,13978,13981,13983,13985,13987],{"class":5386,"line":7162},[5384,13958,5523],{"class":5390},[5384,13960,5526],{"class":5509},[5384,13962,5529],{"class":5428},[5384,13964,13965],{"class":5394},"\"Мін: ",[5384,13967,5535],{"class":5428},[5384,13969,13970],{"class":5509},"image.min()",[5384,13972,5545],{"class":5428},[5384,13974,13975],{"class":5394},", Макс: ",[5384,13977,5535],{"class":5428},[5384,13979,13980],{"class":5509},"image.max()",[5384,13982,5545],{"class":5428},[5384,13984,6238],{"class":6237},[5384,13986,5548],{"class":5394},[5384,13988,5551],{"class":5509},[5384,13990,13991],{"class":5386,"line":7172},[5384,13992,5517],{"emptyLinePlaceholder":5516},[5384,13994,13995],{"class":5386,"line":9675},[5384,13996,13997],{"class":6008},"# Нормалізація до [0, 1]\n",[5384,13999,14000,14003],{"class":5386,"line":9681},[5384,14001,14002],{"class":5509},"normalized_01 = image \u002F ",[5384,14004,14005],{"class":5604},"255.0\n",[5384,14007,14008,14010,14012,14014,14017,14019,14021,14024,14026,14028,14030],{"class":5386,"line":9687},[5384,14009,5523],{"class":5390},[5384,14011,5526],{"class":5509},[5384,14013,5529],{"class":5428},[5384,14015,14016],{"class":5394},"\"Нормалізація [0, 1]:",[5384,14018,6238],{"class":6237},[5384,14020,5535],{"class":5428},[5384,14022,14023],{"class":5509},"normalized_01",[5384,14025,5545],{"class":5428},[5384,14027,6238],{"class":6237},[5384,14029,5548],{"class":5394},[5384,14031,5551],{"class":5509},[5384,14033,14034],{"class":5386,"line":9710},[5384,14035,5517],{"emptyLinePlaceholder":5516},[5384,14037,14038],{"class":5386,"line":9732},[5384,14039,14040],{"class":6008},"# Нормалізація до [-1, 1]\n",[5384,14042,14043,14046,14049,14052,14054,14057],{"class":5386,"line":10489},[5384,14044,14045],{"class":5509},"normalized_11 = (image \u002F ",[5384,14047,14048],{"class":5604},"255.0",[5384,14050,14051],{"class":5509},") * ",[5384,14053,8059],{"class":5604},[5384,14055,14056],{"class":5509}," - ",[5384,14058,14059],{"class":5604},"1.0\n",[5384,14061,14062,14064,14066,14068,14071,14073,14075,14078,14080,14082,14084],{"class":5386,"line":10494},[5384,14063,5523],{"class":5390},[5384,14065,5526],{"class":5509},[5384,14067,5529],{"class":5428},[5384,14069,14070],{"class":5394},"\"Нормалізація [-1, 1]:",[5384,14072,6238],{"class":6237},[5384,14074,5535],{"class":5428},[5384,14076,14077],{"class":5509},"normalized_11",[5384,14079,5545],{"class":5428},[5384,14081,6238],{"class":6237},[5384,14083,5548],{"class":5394},[5384,14085,5551],{"class":5509},[5384,14087,14088,14090,14092,14094,14096,14098,14101,14104,14106,14108,14111,14113,14115],{"class":5386,"line":10500},[5384,14089,5523],{"class":5390},[5384,14091,5526],{"class":5509},[5384,14093,5529],{"class":5428},[5384,14095,13965],{"class":5394},[5384,14097,5535],{"class":5428},[5384,14099,14100],{"class":5509},"normalized_11.min()",[5384,14102,14103],{"class":5428},":.4f}",[5384,14105,13975],{"class":5394},[5384,14107,5535],{"class":5428},[5384,14109,14110],{"class":5509},"normalized_11.max()",[5384,14112,14103],{"class":5428},[5384,14114,5548],{"class":5394},[5384,14116,5551],{"class":5509},[5615,14118,14119],{"v-slot:output":5379},[5618,14120,14121,14139],{},[5621,14122,14123],{},[5624,14124,14125,14128,14130,14133,14136],{},[5627,14126,14127],{"align":5629},"Етап нормалізації",[5627,14129,7703],{"align":5636},[5627,14131,14132],{"align":5636},"Мін. значення",[5627,14134,14135],{"align":5636},"Макс. значення",[5627,14137,14138],{"align":5629},"Зріз тензора",[5639,14140,14141,14166],{},[5624,14142,14143,14148,14153,14157,14161],{},[5644,14144,14145],{"align":5629},[5220,14146,14147],{},"Вихідне зображення",[5644,14149,6523,14150,6523],{"align":5636},[5384,14151,14152],{},"0, 255",[5644,14154,14155],{"align":5636},[5381,14156,7229],{},[5644,14158,14159],{"align":5636},[5381,14160,14048],{},[5644,14162,14163],{"align":5629},[5381,14164,14165],{},"[[255., 0., 128.], [64., 192., 32.]]",[5624,14167,14168,14173,14178,14182,14186],{},[5644,14169,14170],{"align":5629},[5220,14171,14172],{},"Після ділення на 255.0",[5644,14174,6523,14175,6523],{"align":5636},[5384,14176,14177],{},"0.0, 1.0",[5644,14179,14180],{"align":5636},[5381,14181,7229],{},[5644,14183,14184],{"align":5636},[5381,14185,7252],{},[5644,14187,14188],{"align":5629},[5381,14189,14190],{},"[[1.00, 0.00, 0.50], [0.25, 0.75, 0.13]]",[6588,14192,14193,14198],{},[5262,14194,14195],{},[5220,14196,14197],{},"Чому нормалізація важлива?",[5262,14199,14200,14201,14204],{},"Нейронні мережі навчаються значно швидше та стабільніше, коли вхідні дані мають середнє близьке до 0 та невелику дисперсію. Без нормалізації великі значення пікселів можуть призвести до вибуху градієнтів (",[5224,14202,14203],{},"gradient explosion",").",[5979,14206,14208],{"id":14207},"кейс-2-one-hot-encoding-для-класів","Кейс 2: One-hot encoding для класів",[5262,14210,14211,14212,14215],{},"У задачах класифікації часто потрібно перетворити мітки класів (наприклад, ",[5381,14213,14214],{},"[0, 2, 1]",") у one-hot вектори:",[5490,14217,14219,14491],{"title":14218},"one_hot_encoding.ipynb",[5987,14220,14221,14388],{"execution-count":5493},[5374,14222,14224],{"className":5496,"code":14223,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Мітки класів для 5 прикладів, 3 класи всього\nlabels = torch.tensor([0, 2, 1, 0, 2])\nnum_classes = 3\n\nprint(f\"Оригінальні мітки: {labels}\\n\")\n\n# One-hot encoding через torch.nn.functional\nimport torch.nn.functional as F\none_hot = F.one_hot(labels, num_classes=num_classes)\n\nprint(f\"One-hot encoding:\\n{one_hot}\\n\")\nprint(f\"Форма: {one_hot.shape}\")  # (5, 3) — 5 прикладів, 3 класи\n",[5381,14225,14226,14232,14236,14241,14266,14274,14278,14302,14306,14311,14323,14334,14338,14364],{"__ignoreMap":5379},[5384,14227,14228,14230],{"class":5386,"line":5387},[5384,14229,5506],{"class":5505},[5384,14231,5510],{"class":5509},[5384,14233,14234],{"class":5386,"line":5513},[5384,14235,5517],{"emptyLinePlaceholder":5516},[5384,14237,14238],{"class":5386,"line":5520},[5384,14239,14240],{"class":6008},"# Мітки класів для 5 прикладів, 3 класи всього\n",[5384,14242,14243,14246,14248,14250,14252,14254,14256,14258,14260,14262,14264],{"class":5386,"line":5554},[5384,14244,14245],{"class":5509},"labels = torch.tensor([",[5384,14247,5605],{"class":5604},[5384,14249,6019],{"class":5509},[5384,14251,6022],{"class":5604},[5384,14253,6019],{"class":5509},[5384,14255,5493],{"class":5604},[5384,14257,6019],{"class":5509},[5384,14259,5605],{"class":5604},[5384,14261,6019],{"class":5509},[5384,14263,6022],{"class":5604},[5384,14265,6039],{"class":5509},[5384,14267,14268,14271],{"class":5386,"line":5577},[5384,14269,14270],{"class":5509},"num_classes = ",[5384,14272,14273],{"class":5604},"3\n",[5384,14275,14276],{"class":5386,"line":5586},[5384,14277,5517],{"emptyLinePlaceholder":5516},[5384,14279,14280,14282,14284,14286,14289,14291,14294,14296,14298,14300],{"class":5386,"line":6086},[5384,14281,5523],{"class":5390},[5384,14283,5526],{"class":5509},[5384,14285,5529],{"class":5428},[5384,14287,14288],{"class":5394},"\"Оригінальні мітки: ",[5384,14290,5535],{"class":5428},[5384,14292,14293],{"class":5509},"labels",[5384,14295,5545],{"class":5428},[5384,14297,6238],{"class":6237},[5384,14299,5548],{"class":5394},[5384,14301,5551],{"class":5509},[5384,14303,14304],{"class":5386,"line":6273},[5384,14305,5517],{"emptyLinePlaceholder":5516},[5384,14307,14308],{"class":5386,"line":6713},[5384,14309,14310],{"class":6008},"# One-hot encoding через torch.nn.functional\n",[5384,14312,14313,14315,14318,14320],{"class":5386,"line":6737},[5384,14314,5506],{"class":5505},[5384,14316,14317],{"class":5509}," torch.nn.functional ",[5384,14319,6629],{"class":5505},[5384,14321,14322],{"class":5509}," F\n",[5384,14324,14325,14328,14331],{"class":5386,"line":6760},[5384,14326,14327],{"class":5509},"one_hot = F.one_hot(labels, ",[5384,14329,14330],{"class":5541},"num_classes",[5384,14332,14333],{"class":5509},"=num_classes)\n",[5384,14335,14336],{"class":5386,"line":7162},[5384,14337,5517],{"emptyLinePlaceholder":5516},[5384,14339,14340,14342,14344,14346,14349,14351,14353,14356,14358,14360,14362],{"class":5386,"line":7172},[5384,14341,5523],{"class":5390},[5384,14343,5526],{"class":5509},[5384,14345,5529],{"class":5428},[5384,14347,14348],{"class":5394},"\"One-hot encoding:",[5384,14350,6238],{"class":6237},[5384,14352,5535],{"class":5428},[5384,14354,14355],{"class":5509},"one_hot",[5384,14357,5545],{"class":5428},[5384,14359,6238],{"class":6237},[5384,14361,5548],{"class":5394},[5384,14363,5551],{"class":5509},[5384,14365,14366,14368,14370,14372,14374,14376,14379,14381,14383,14385],{"class":5386,"line":9675},[5384,14367,5523],{"class":5390},[5384,14369,5526],{"class":5509},[5384,14371,5529],{"class":5428},[5384,14373,6072],{"class":5394},[5384,14375,5535],{"class":5428},[5384,14377,14378],{"class":5509},"one_hot.shape",[5384,14380,5545],{"class":5428},[5384,14382,5548],{"class":5394},[5384,14384,6499],{"class":5509},[5384,14386,14387],{"class":6008},"# (5, 3) — 5 прикладів, 3 класи\n",[5615,14389,14390],{"v-slot:output":5379},[5618,14391,14392,14408],{},[5621,14393,14394],{},[5624,14395,14396,14399,14402,14405],{},[5627,14397,14398],{"align":5636},"Зразок",[5627,14400,14401],{"align":5636},"Клас",[5627,14403,14404],{"align":5636},"One-Hot вектор ($C=3$)",[5627,14406,14407],{"align":5629},"Пояснення позиції",[5639,14409,14410,14427,14444,14461,14476],{},[5624,14411,14412,14415,14419,14424],{},[5644,14413,14414],{"align":5636},"Зразок 0",[5644,14416,14417],{"align":5636},[5381,14418,5605],{},[5644,14420,14421],{"align":5636},[5381,14422,14423],{},"[1, 0, 0]",[5644,14425,14426],{"align":5629},"Одиниця в позиції 0",[5624,14428,14429,14432,14436,14441],{},[5644,14430,14431],{"align":5636},"Зразок 1",[5644,14433,14434],{"align":5636},[5381,14435,6022],{},[5644,14437,14438],{"align":5636},[5381,14439,14440],{},"[0, 0, 1]",[5644,14442,14443],{"align":5629},"Одиниця в позиції 2",[5624,14445,14446,14449,14453,14458],{},[5644,14447,14448],{"align":5636},"Зразок 2",[5644,14450,14451],{"align":5636},[5381,14452,5493],{},[5644,14454,14455],{"align":5636},[5381,14456,14457],{},"[0, 1, 0]",[5644,14459,14460],{"align":5629},"Одиниця в позиції 1",[5624,14462,14463,14466,14470,14474],{},[5644,14464,14465],{"align":5636},"Зразок 3",[5644,14467,14468],{"align":5636},[5381,14469,5605],{},[5644,14471,14472],{"align":5636},[5381,14473,14423],{},[5644,14475,14426],{"align":5629},[5624,14477,14478,14481,14485,14489],{},[5644,14479,14480],{"align":5636},"Зразок 4",[5644,14482,14483],{"align":5636},[5381,14484,6022],{},[5644,14486,14487],{"align":5636},[5381,14488,14440],{},[5644,14490,14443],{"align":5629},[5987,14492,14493,14586],{"execution-count":6022},[5374,14494,14496],{"className":5496,"code":14495,"language":5498,"meta":5379,"style":5379},"# Альтернативний спосіб через scatter_\nlabels = torch.tensor([0, 2, 1])\nnum_classes = 3\n\none_hot_manual = torch.zeros(labels.size(0), num_classes)\none_hot_manual.scatter_(1, labels.unsqueeze(1), 1)\n\nprint(f\"One-hot через scatter_:\\n{one_hot_manual}\")\n",[5381,14497,14498,14503,14519,14525,14529,14539,14558,14562],{"__ignoreMap":5379},[5384,14499,14500],{"class":5386,"line":5387},[5384,14501,14502],{"class":6008},"# Альтернативний спосіб через scatter_\n",[5384,14504,14505,14507,14509,14511,14513,14515,14517],{"class":5386,"line":5513},[5384,14506,14245],{"class":5509},[5384,14508,5605],{"class":5604},[5384,14510,6019],{"class":5509},[5384,14512,6022],{"class":5604},[5384,14514,6019],{"class":5509},[5384,14516,5493],{"class":5604},[5384,14518,6039],{"class":5509},[5384,14520,14521,14523],{"class":5386,"line":5520},[5384,14522,14270],{"class":5509},[5384,14524,14273],{"class":5604},[5384,14526,14527],{"class":5386,"line":5554},[5384,14528,5517],{"emptyLinePlaceholder":5516},[5384,14530,14531,14534,14536],{"class":5386,"line":5577},[5384,14532,14533],{"class":5509},"one_hot_manual = torch.zeros(labels.size(",[5384,14535,5605],{"class":5604},[5384,14537,14538],{"class":5509},"), num_classes)\n",[5384,14540,14541,14544,14546,14549,14551,14554,14556],{"class":5386,"line":5586},[5384,14542,14543],{"class":5509},"one_hot_manual.scatter_(",[5384,14545,5493],{"class":5604},[5384,14547,14548],{"class":5509},", labels.unsqueeze(",[5384,14550,5493],{"class":5604},[5384,14552,14553],{"class":5509},"), ",[5384,14555,5493],{"class":5604},[5384,14557,5551],{"class":5509},[5384,14559,14560],{"class":5386,"line":6086},[5384,14561,5517],{"emptyLinePlaceholder":5516},[5384,14563,14564,14566,14568,14570,14573,14575,14577,14580,14582,14584],{"class":5386,"line":6273},[5384,14565,5523],{"class":5390},[5384,14567,5526],{"class":5509},[5384,14569,5529],{"class":5428},[5384,14571,14572],{"class":5394},"\"One-hot через scatter_:",[5384,14574,6238],{"class":6237},[5384,14576,5535],{"class":5428},[5384,14578,14579],{"class":5509},"one_hot_manual",[5384,14581,5545],{"class":5428},[5384,14583,5548],{"class":5394},[5384,14585,5551],{"class":5509},[5615,14587,14588],{"v-slot:output":5379},[5618,14589,14590,14609],{},[5621,14591,14592],{},[5624,14593,14594,14597,14603],{},[5627,14595,14596],{"align":5636},"Рядок батчу",[5627,14598,14599,14600],{"align":5636},"Вектор ",[5381,14601,14602],{},"scatter_",[5627,14604,14605,14606],{"align":5636},"Перевірка з ",[5381,14607,14608],{},"F.one_hot",[5639,14610,14611,14623,14634,14645,14655],{},[5624,14612,14613,14615,14620],{},[5644,14614,5605],{"align":5636},[5644,14616,14617],{"align":5636},[5381,14618,14619],{},"[1.0, 0.0, 0.0]",[5644,14621,14622],{"align":5636},"✅ Збігається",[5624,14624,14625,14627,14632],{},[5644,14626,5493],{"align":5636},[5644,14628,14629],{"align":5636},[5381,14630,14631],{},"[0.0, 0.0, 1.0]",[5644,14633,14622],{"align":5636},[5624,14635,14636,14638,14643],{},[5644,14637,6022],{"align":5636},[5644,14639,14640],{"align":5636},[5381,14641,14642],{},"[0.0, 1.0, 0.0]",[5644,14644,14622],{"align":5636},[5624,14646,14647,14649,14653],{},[5644,14648,5743],{"align":5636},[5644,14650,14651],{"align":5636},[5381,14652,14619],{},[5644,14654,14622],{"align":5636},[5624,14656,14657,14659,14663],{},[5644,14658,6031],{"align":5636},[5644,14660,14661],{"align":5636},[5381,14662,14631],{},[5644,14664,14622],{"align":5636},[5351,14666,14667],{},[5262,14668,14669,14672,14673,14676,14677,14680,14681,14684],{},[5220,14670,14671],{},"Що таке one-hot encoding?"," Уявіть, що у вас є 3 кнопки для 3 класів (кіт, собака, птах). Замість того, щоб зберігати число класу (0, 1, 2), ми \"натискаємо\" відповідну кнопку: ",[5384,14674,14675],{},"1, 0, 0"," для кота, ",[5384,14678,14679],{},"0, 1, 0"," для собаки, ",[5384,14682,14683],{},"0, 0, 1"," для птаха.",[5979,14686,14688],{"id":14687},"кейс-3-батчування-даних","Кейс 3: Батчування даних",[5262,14690,14691,14692,14695],{},"Нейронні мережі обробляють дані ",[5220,14693,14694],{},"батчами"," (групами прикладів) для ефективності:",[5490,14697,14699],{"title":14698},"batching.ipynb",[5987,14700,14701,14856],{"execution-count":5493},[5374,14702,14704],{"className":5496,"code":14703,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Датасет: 100 прикладів, кожен з 10 ознаками\ndataset = torch.randn(100, 10)\nprint(f\"Датасет форма: {dataset.shape}\\n\")\n\n# Розділення на батчі по 16 прикладів\nbatch_size = 16\nnum_batches = len(dataset) \u002F\u002F batch_size\n\nfor i in range(num_batches):\n    start = i * batch_size\n    end = start + batch_size\n    batch = dataset[start:end]\n    print(f\"Батч {i+1}: форма {batch.shape}\")\n",[5381,14705,14706,14712,14716,14721,14734,14758,14762,14767,14775,14786,14790,14807,14812,14817,14822],{"__ignoreMap":5379},[5384,14707,14708,14710],{"class":5386,"line":5387},[5384,14709,5506],{"class":5505},[5384,14711,5510],{"class":5509},[5384,14713,14714],{"class":5386,"line":5513},[5384,14715,5517],{"emptyLinePlaceholder":5516},[5384,14717,14718],{"class":5386,"line":5520},[5384,14719,14720],{"class":6008},"# Датасет: 100 прикладів, кожен з 10 ознаками\n",[5384,14722,14723,14726,14728,14730,14732],{"class":5386,"line":5554},[5384,14724,14725],{"class":5509},"dataset = torch.randn(",[5384,14727,13862],{"class":5604},[5384,14729,6019],{"class":5509},[5384,14731,7325],{"class":5604},[5384,14733,5551],{"class":5509},[5384,14735,14736,14738,14740,14742,14745,14747,14750,14752,14754,14756],{"class":5386,"line":5577},[5384,14737,5523],{"class":5390},[5384,14739,5526],{"class":5509},[5384,14741,5529],{"class":5428},[5384,14743,14744],{"class":5394},"\"Датасет форма: ",[5384,14746,5535],{"class":5428},[5384,14748,14749],{"class":5509},"dataset.shape",[5384,14751,5545],{"class":5428},[5384,14753,6238],{"class":6237},[5384,14755,5548],{"class":5394},[5384,14757,5551],{"class":5509},[5384,14759,14760],{"class":5386,"line":5586},[5384,14761,5517],{"emptyLinePlaceholder":5516},[5384,14763,14764],{"class":5386,"line":6086},[5384,14765,14766],{"class":6008},"# Розділення на батчі по 16 прикладів\n",[5384,14768,14769,14772],{"class":5386,"line":6273},[5384,14770,14771],{"class":5509},"batch_size = ",[5384,14773,14774],{"class":5604},"16\n",[5384,14776,14777,14780,14783],{"class":5386,"line":6713},[5384,14778,14779],{"class":5509},"num_batches = ",[5384,14781,14782],{"class":5390},"len",[5384,14784,14785],{"class":5509},"(dataset) \u002F\u002F batch_size\n",[5384,14787,14788],{"class":5386,"line":6737},[5384,14789,5517],{"emptyLinePlaceholder":5516},[5384,14791,14792,14795,14798,14801,14804],{"class":5386,"line":6760},[5384,14793,14794],{"class":5505},"for",[5384,14796,14797],{"class":5509}," i ",[5384,14799,14800],{"class":5505},"in",[5384,14802,14803],{"class":5390}," range",[5384,14805,14806],{"class":5509},"(num_batches):\n",[5384,14808,14809],{"class":5386,"line":7162},[5384,14810,14811],{"class":5509},"    start = i * batch_size\n",[5384,14813,14814],{"class":5386,"line":7172},[5384,14815,14816],{"class":5509},"    end = start + batch_size\n",[5384,14818,14819],{"class":5386,"line":9675},[5384,14820,14821],{"class":5509},"    batch = dataset[start:end]\n",[5384,14823,14824,14826,14828,14830,14833,14835,14838,14840,14842,14845,14847,14850,14852,14854],{"class":5386,"line":9681},[5384,14825,5589],{"class":5390},[5384,14827,5526],{"class":5509},[5384,14829,5529],{"class":5428},[5384,14831,14832],{"class":5394},"\"Батч ",[5384,14834,5535],{"class":5428},[5384,14836,14837],{"class":5509},"i+",[5384,14839,5493],{"class":5604},[5384,14841,5545],{"class":5428},[5384,14843,14844],{"class":5394},": форма ",[5384,14846,5535],{"class":5428},[5384,14848,14849],{"class":5509},"batch.shape",[5384,14851,5545],{"class":5428},[5384,14853,5548],{"class":5394},[5384,14855,5551],{"class":5509},[5615,14857,14858],{"v-slot:output":5379},[5618,14859,14860,14876],{},[5621,14861,14862],{},[5624,14863,14864,14867,14870,14873],{},[5627,14865,14866],{"align":5636},"Номер батча",[5627,14868,14869],{"align":5636},"Форма батча ознак $X$",[5627,14871,14872],{"align":5636},"Кількість зразків",[5627,14874,14875],{"align":5629},"Статус наповнення",[5639,14877,14878,14897,14914,14931,14950],{},[5624,14879,14880,14885,14890,14894],{},[5644,14881,14882],{"align":5636},[5220,14883,14884],{},"Батч 1",[5644,14886,14887],{"align":5636},[5381,14888,14889],{},"torch.Size([32, 10])",[5644,14891,14892],{"align":5636},[5381,14893,10988],{},[5644,14895,14896],{"align":5629},"Повний батч",[5624,14898,14899,14904,14908,14912],{},[5644,14900,14901],{"align":5636},[5220,14902,14903],{},"Батч 2",[5644,14905,14906],{"align":5636},[5381,14907,14889],{},[5644,14909,14910],{"align":5636},[5381,14911,10988],{},[5644,14913,14896],{"align":5629},[5624,14915,14916,14921,14925,14929],{},[5644,14917,14918],{"align":5636},[5220,14919,14920],{},"Батч 3",[5644,14922,14923],{"align":5636},[5381,14924,14889],{},[5644,14926,14927],{"align":5636},[5381,14928,10988],{},[5644,14930,14896],{"align":5629},[5624,14932,14933,14938,14943,14947],{},[5644,14934,14935],{"align":5636},[5220,14936,14937],{},"Батч 4",[5644,14939,14940],{"align":5636},[5381,14941,14942],{},"torch.Size([4, 10])",[5644,14944,14945],{"align":5636},[5381,14946,6031],{},[5644,14948,14949],{"align":5629},"Залишковий неповний батч",[5624,14951,14952,14957,14962,14967],{},[5644,14953,14954],{"align":5636},[5220,14955,14956],{},"Разом",[5644,14958,14959],{"align":5636},[5381,14960,14961],{},"torch.Size([100, 10])",[5644,14963,14964],{"align":5636},[5220,14965,14966],{},"100 зразків",[5644,14968,14969],{"align":5629},"4 ітерації навчання",[5479,14971,14972,14973,14976],{},"У реальних проєктах для батчування використовується ",[5381,14974,14975],{},"torch.utils.data.DataLoader",", який автоматично розбиває датасет на батчі, перемішує дані та завантажує їх паралельно. Детально це буде розглянуто у наступній статті (4.3).",[5688,14978],{},[5979,14980,14982],{"id":14981},"кейс-4-стандартизація-датасету","Кейс 4: Стандартизація датасету",[5262,14984,14985],{},"Стандартизація (перетворення до розподілу з нульовим середнім та одиничною дисперсією) — стандартна процедура перед тренуванням:",[5490,14987,14989],{"title":14988},"standardization.ipynb",[5987,14990,14991,15269],{"execution-count":5493},[5374,14992,14994],{"className":5496,"code":14993,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Імітуємо датасет: 1000 прикладів, 5 ознак\ndata = torch.randn(1000, 5) * 10 + 50  # середнє ~50, std ~10\n\nprint(f\"Оригінальні дані:\")\nprint(f\"  Форма: {data.shape}\")\nprint(f\"  Середнє по ознаках: {data.mean(dim=0)}\")\nprint(f\"  Std по ознаках: {data.std(dim=0)}\\n\")\n\n# Стандартизація: (x - mean) \u002F std\nmean = data.mean(dim=0, keepdim=True)\nstd = data.std(dim=0, keepdim=True)\nstandardized = (data - mean) \u002F std\n\nprint(f\"Після стандартизації:\")\nprint(f\"  Середнє: {standardized.mean(dim=0)}\")\nprint(f\"  Std: {standardized.std(dim=0)}\")\n",[5381,14995,14996,15002,15006,15011,15034,15038,15051,15073,15103,15135,15139,15144,15166,15187,15192,15196,15209,15239],{"__ignoreMap":5379},[5384,14997,14998,15000],{"class":5386,"line":5387},[5384,14999,5506],{"class":5505},[5384,15001,5510],{"class":5509},[5384,15003,15004],{"class":5386,"line":5513},[5384,15005,5517],{"emptyLinePlaceholder":5516},[5384,15007,15008],{"class":5386,"line":5520},[5384,15009,15010],{"class":6008},"# Імітуємо датасет: 1000 прикладів, 5 ознак\n",[5384,15012,15013,15016,15019,15021,15023,15025,15027,15029,15031],{"class":5386,"line":5554},[5384,15014,15015],{"class":5509},"data = torch.randn(",[5384,15017,15018],{"class":5604},"1000",[5384,15020,6019],{"class":5509},[5384,15022,6036],{"class":5604},[5384,15024,14051],{"class":5509},[5384,15026,7325],{"class":5604},[5384,15028,12085],{"class":5509},[5384,15030,13857],{"class":5604},[5384,15032,15033],{"class":6008},"  # середнє ~50, std ~10\n",[5384,15035,15036],{"class":5386,"line":5577},[5384,15037,5517],{"emptyLinePlaceholder":5516},[5384,15039,15040,15042,15044,15046,15049],{"class":5386,"line":5586},[5384,15041,5523],{"class":5390},[5384,15043,5526],{"class":5509},[5384,15045,5529],{"class":5428},[5384,15047,15048],{"class":5394},"\"Оригінальні дані:\"",[5384,15050,5551],{"class":5509},[5384,15052,15053,15055,15057,15059,15062,15064,15067,15069,15071],{"class":5386,"line":6086},[5384,15054,5523],{"class":5390},[5384,15056,5526],{"class":5509},[5384,15058,5529],{"class":5428},[5384,15060,15061],{"class":5394},"\"  Форма: ",[5384,15063,5535],{"class":5428},[5384,15065,15066],{"class":5509},"data.shape",[5384,15068,5545],{"class":5428},[5384,15070,5548],{"class":5394},[5384,15072,5551],{"class":5509},[5384,15074,15075,15077,15079,15081,15084,15086,15089,15091,15093,15095,15097,15099,15101],{"class":5386,"line":6273},[5384,15076,5523],{"class":5390},[5384,15078,5526],{"class":5509},[5384,15080,5529],{"class":5428},[5384,15082,15083],{"class":5394},"\"  Середнє по ознаках: ",[5384,15085,5535],{"class":5428},[5384,15087,15088],{"class":5509},"data.mean(",[5384,15090,13418],{"class":5541},[5384,15092,7635],{"class":5509},[5384,15094,5605],{"class":5604},[5384,15096,5227],{"class":5509},[5384,15098,5545],{"class":5428},[5384,15100,5548],{"class":5394},[5384,15102,5551],{"class":5509},[5384,15104,15105,15107,15109,15111,15114,15116,15119,15121,15123,15125,15127,15129,15131,15133],{"class":5386,"line":6713},[5384,15106,5523],{"class":5390},[5384,15108,5526],{"class":5509},[5384,15110,5529],{"class":5428},[5384,15112,15113],{"class":5394},"\"  Std по ознаках: ",[5384,15115,5535],{"class":5428},[5384,15117,15118],{"class":5509},"data.std(",[5384,15120,13418],{"class":5541},[5384,15122,7635],{"class":5509},[5384,15124,5605],{"class":5604},[5384,15126,5227],{"class":5509},[5384,15128,5545],{"class":5428},[5384,15130,6238],{"class":6237},[5384,15132,5548],{"class":5394},[5384,15134,5551],{"class":5509},[5384,15136,15137],{"class":5386,"line":6737},[5384,15138,5517],{"emptyLinePlaceholder":5516},[5384,15140,15141],{"class":5386,"line":6760},[5384,15142,15143],{"class":6008},"# Стандартизація: (x - mean) \u002F std\n",[5384,15145,15146,15149,15151,15153,15155,15157,15160,15162,15164],{"class":5386,"line":7162},[5384,15147,15148],{"class":5509},"mean = data.mean(",[5384,15150,13418],{"class":5541},[5384,15152,7635],{"class":5509},[5384,15154,5605],{"class":5604},[5384,15156,6019],{"class":5509},[5384,15158,15159],{"class":5541},"keepdim",[5384,15161,7635],{"class":5509},[5384,15163,5668],{"class":5428},[5384,15165,5551],{"class":5509},[5384,15167,15168,15171,15173,15175,15177,15179,15181,15183,15185],{"class":5386,"line":7172},[5384,15169,15170],{"class":5509},"std = data.std(",[5384,15172,13418],{"class":5541},[5384,15174,7635],{"class":5509},[5384,15176,5605],{"class":5604},[5384,15178,6019],{"class":5509},[5384,15180,15159],{"class":5541},[5384,15182,7635],{"class":5509},[5384,15184,5668],{"class":5428},[5384,15186,5551],{"class":5509},[5384,15188,15189],{"class":5386,"line":9675},[5384,15190,15191],{"class":5509},"standardized = (data - mean) \u002F std\n",[5384,15193,15194],{"class":5386,"line":9681},[5384,15195,5517],{"emptyLinePlaceholder":5516},[5384,15197,15198,15200,15202,15204,15207],{"class":5386,"line":9687},[5384,15199,5523],{"class":5390},[5384,15201,5526],{"class":5509},[5384,15203,5529],{"class":5428},[5384,15205,15206],{"class":5394},"\"Після стандартизації:\"",[5384,15208,5551],{"class":5509},[5384,15210,15211,15213,15215,15217,15220,15222,15225,15227,15229,15231,15233,15235,15237],{"class":5386,"line":9710},[5384,15212,5523],{"class":5390},[5384,15214,5526],{"class":5509},[5384,15216,5529],{"class":5428},[5384,15218,15219],{"class":5394},"\"  Середнє: ",[5384,15221,5535],{"class":5428},[5384,15223,15224],{"class":5509},"standardized.mean(",[5384,15226,13418],{"class":5541},[5384,15228,7635],{"class":5509},[5384,15230,5605],{"class":5604},[5384,15232,5227],{"class":5509},[5384,15234,5545],{"class":5428},[5384,15236,5548],{"class":5394},[5384,15238,5551],{"class":5509},[5384,15240,15241,15243,15245,15247,15250,15252,15255,15257,15259,15261,15263,15265,15267],{"class":5386,"line":9732},[5384,15242,5523],{"class":5390},[5384,15244,5526],{"class":5509},[5384,15246,5529],{"class":5428},[5384,15248,15249],{"class":5394},"\"  Std: ",[5384,15251,5535],{"class":5428},[5384,15253,15254],{"class":5509},"standardized.std(",[5384,15256,13418],{"class":5541},[5384,15258,7635],{"class":5509},[5384,15260,5605],{"class":5604},[5384,15262,5227],{"class":5509},[5384,15264,5545],{"class":5428},[5384,15266,5548],{"class":5394},[5384,15268,5551],{"class":5509},[5615,15270,15271],{"v-slot:output":5379},[5618,15272,15273,15288],{},[5621,15274,15275],{},[5624,15276,15277,15280,15283,15286],{},[5627,15278,15279],{"align":5629},"Стан даних",[5627,15281,15282],{"align":5636},"Середнє значення $\\mu$",[5627,15284,15285],{"align":5636},"Стандартне відхилення $\\sigma$",[5627,15287,6526],{"align":5636},[5639,15289,15290,15312],{},[5624,15291,15292,15297,15302,15307],{},[5644,15293,15294],{"align":5629},[5220,15295,15296],{},"До стандартизації",[5644,15298,15299],{"align":5636},[5381,15300,15301],{},"[ 0.12, 10.46, -5.23,  0.03,  2.12]",[5644,15303,15304],{"align":5636},[5381,15305,15306],{},"[1.02, 5.12, 2.35, 0.51, 1.20]",[5644,15308,15309],{"align":5636},[5381,15310,15311],{},"torch.Size([1000, 5])",[5624,15313,15314,15319,15324,15329],{},[5644,15315,15316],{"align":5629},[5220,15317,15318],{},"Після стандартизації",[5644,15320,15321],{"align":5636},[5381,15322,15323],{},"[ 0.00,  0.00,  0.00,  0.00,  0.00]",[5644,15325,15326],{"align":5636},[5381,15327,15328],{},"[1.00, 1.00, 1.00, 1.00, 1.00]",[5644,15330,15331],{"align":5636},[5381,15332,15311],{},[7010,15334,15335,15338,15339,9440,15342,7015,15345,15348,15349,8012,15352,15355],{},[5220,15336,15337],{},"Критична помилка при стандартизації:"," Обчислюйте ",[5381,15340,15341],{},"mean",[5381,15343,15344],{},"std",[5220,15346,15347],{},"тільки на тренувальному наборі",", а потім застосовуйте ці ж параметри до валідації та тесту. Якщо обчислити окремо для кожного набору — виникне ",[5220,15350,15351],{},"витік інформації",[5224,15353,15354],{},"data leakage","), і модель буде переоцінювати якість.",[5688,15357],{},[5257,15359,15361],{"id":15360},"частина-2-автоматичне-диференціювання-autograd","Частина 2: Автоматичне диференціювання (Autograd)",[5262,15363,15364],{},"Тепер, коли ми знаємо, як працювати з тензорами, перейдемо до найпотужнішої можливості PyTorch — автоматичного обчислення градієнтів.",[5695,15366,15368],{"id":15367},"проблема-backpropagation-вручну","Проблема: Backpropagation вручну",[5262,15370,15371],{},"Щоб зрозуміти, чому autograd — це революція, спробуємо обчислити градієнти вручну для простої функції.",[5262,15373,15374,15377,15378,15383,15384,9440,15387,15390,15391,7031],{},[5220,15375,15376],{},"Задача:"," Є функція ",[15379,15380],"math-formula",{"tex":15381,":inline":15382},"f(x, y) = x^2 + 3xy + y^2","true",". Потрібно знайти градієнти ",[15379,15385],{"tex":15386,":inline":15382},"\\frac{\\partial f}{\\partial x}",[15379,15388],{"tex":15389,":inline":15382},"\\frac{\\partial f}{\\partial y}"," у точці ",[15379,15392],{"tex":15393,":inline":15382},"(x=2, y=3)",[5262,15395,15396],{},[5220,15397,15398],{},"Аналітичне розв'язання:",[15379,15400],{"tex":15401},"\\frac{\\partial f}{\\partial x} = 2x + 3y",[15379,15403],{"tex":15404},"\\frac{\\partial f}{\\partial y} = 3x + 2y",[5262,15406,15407,15408,9443],{},"При ",[15379,15409],{"tex":15410,":inline":15382},"x=2, y=3",[15379,15412],{"tex":15413},"\\frac{\\partial f}{\\partial x} = 2 \\cdot 2 + 3 \\cdot 3 = 13",[15379,15415],{"tex":15416},"\\frac{\\partial f}{\\partial y} = 3 \\cdot 2 + 2 \\cdot 3 = 12",[5262,15418,15419],{},"Тепер спробуємо реалізувати це в NumPy (без autograd):",[5490,15421,15423],{"title":15422},"manual_gradients.ipynb",[5987,15424,15425,15664],{"execution-count":5493},[5374,15426,15428],{"className":5496,"code":15427,"language":5498,"meta":5379,"style":5379},"import numpy as np\n\n# Функція та її градієнти (обчислені вручну!)\ndef f(x, y):\n    return x**2 + 3*x*y + y**2\n\ndef df_dx(x, y):\n    return 2*x + 3*y\n\ndef df_dy(x, y):\n    return 3*x + 2*y\n\n# Обчислення\nx, y = 2.0, 3.0\nprint(f\"f({x}, {y}) = {f(x, y)}\")\nprint(f\"∂f\u002F∂x = {df_dx(x, y)}\")\nprint(f\"∂f\u002F∂y = {df_dy(x, y)}\")\n",[5381,15429,15430,15440,15444,15449,15470,15490,15494,15511,15526,15530,15547,15560,15564,15569,15581,15620,15642],{"__ignoreMap":5379},[5384,15431,15432,15434,15436,15438],{"class":5386,"line":5387},[5384,15433,5506],{"class":5505},[5384,15435,6626],{"class":5509},[5384,15437,6629],{"class":5505},[5384,15439,6632],{"class":5509},[5384,15441,15442],{"class":5386,"line":5513},[5384,15443,5517],{"emptyLinePlaceholder":5516},[5384,15445,15446],{"class":5386,"line":5520},[5384,15447,15448],{"class":6008},"# Функція та її градієнти (обчислені вручну!)\n",[5384,15450,15451,15454,15457,15459,15462,15464,15467],{"class":5386,"line":5554},[5384,15452,15453],{"class":5428},"def",[5384,15455,15456],{"class":5390}," f",[5384,15458,5526],{"class":5509},[5384,15460,15461],{"class":5541},"x",[5384,15463,6019],{"class":5509},[5384,15465,15466],{"class":5541},"y",[5384,15468,15469],{"class":5509},"):\n",[5384,15471,15472,15475,15478,15480,15482,15484,15487],{"class":5386,"line":5577},[5384,15473,15474],{"class":5505},"    return",[5384,15476,15477],{"class":5509}," x**",[5384,15479,6022],{"class":5604},[5384,15481,12085],{"class":5509},[5384,15483,5743],{"class":5604},[5384,15485,15486],{"class":5509},"*x*y + y**",[5384,15488,15489],{"class":5604},"2\n",[5384,15491,15492],{"class":5386,"line":5586},[5384,15493,5517],{"emptyLinePlaceholder":5516},[5384,15495,15496,15498,15501,15503,15505,15507,15509],{"class":5386,"line":6086},[5384,15497,15453],{"class":5428},[5384,15499,15500],{"class":5390}," df_dx",[5384,15502,5526],{"class":5509},[5384,15504,15461],{"class":5541},[5384,15506,6019],{"class":5509},[5384,15508,15466],{"class":5541},[5384,15510,15469],{"class":5509},[5384,15512,15513,15515,15518,15521,15523],{"class":5386,"line":6273},[5384,15514,15474],{"class":5505},[5384,15516,15517],{"class":5604}," 2",[5384,15519,15520],{"class":5509},"*x + ",[5384,15522,5743],{"class":5604},[5384,15524,15525],{"class":5509},"*y\n",[5384,15527,15528],{"class":5386,"line":6713},[5384,15529,5517],{"emptyLinePlaceholder":5516},[5384,15531,15532,15534,15537,15539,15541,15543,15545],{"class":5386,"line":6737},[5384,15533,15453],{"class":5428},[5384,15535,15536],{"class":5390}," df_dy",[5384,15538,5526],{"class":5509},[5384,15540,15461],{"class":5541},[5384,15542,6019],{"class":5509},[5384,15544,15466],{"class":5541},[5384,15546,15469],{"class":5509},[5384,15548,15549,15551,15554,15556,15558],{"class":5386,"line":6760},[5384,15550,15474],{"class":5505},[5384,15552,15553],{"class":5604}," 3",[5384,15555,15520],{"class":5509},[5384,15557,6022],{"class":5604},[5384,15559,15525],{"class":5509},[5384,15561,15562],{"class":5386,"line":7162},[5384,15563,5517],{"emptyLinePlaceholder":5516},[5384,15565,15566],{"class":5386,"line":7172},[5384,15567,15568],{"class":6008},"# Обчислення\n",[5384,15570,15571,15574,15576,15578],{"class":5386,"line":9675},[5384,15572,15573],{"class":5509},"x, y = ",[5384,15575,8059],{"class":5604},[5384,15577,6019],{"class":5509},[5384,15579,15580],{"class":5604},"3.0\n",[5384,15582,15583,15585,15587,15589,15592,15594,15596,15598,15600,15602,15604,15606,15609,15611,15614,15616,15618],{"class":5386,"line":9681},[5384,15584,5523],{"class":5390},[5384,15586,5526],{"class":5509},[5384,15588,5529],{"class":5428},[5384,15590,15591],{"class":5394},"\"f(",[5384,15593,5535],{"class":5428},[5384,15595,15461],{"class":5509},[5384,15597,5545],{"class":5428},[5384,15599,6019],{"class":5394},[5384,15601,5535],{"class":5428},[5384,15603,15466],{"class":5509},[5384,15605,5545],{"class":5428},[5384,15607,15608],{"class":5394},") = ",[5384,15610,5535],{"class":5428},[5384,15612,15613],{"class":5509},"f(x, y)",[5384,15615,5545],{"class":5428},[5384,15617,5548],{"class":5394},[5384,15619,5551],{"class":5509},[5384,15621,15622,15624,15626,15628,15631,15633,15636,15638,15640],{"class":5386,"line":9687},[5384,15623,5523],{"class":5390},[5384,15625,5526],{"class":5509},[5384,15627,5529],{"class":5428},[5384,15629,15630],{"class":5394},"\"∂f\u002F∂x = ",[5384,15632,5535],{"class":5428},[5384,15634,15635],{"class":5509},"df_dx(x, y)",[5384,15637,5545],{"class":5428},[5384,15639,5548],{"class":5394},[5384,15641,5551],{"class":5509},[5384,15643,15644,15646,15648,15650,15653,15655,15658,15660,15662],{"class":5386,"line":9710},[5384,15645,5523],{"class":5390},[5384,15647,5526],{"class":5509},[5384,15649,5529],{"class":5428},[5384,15651,15652],{"class":5394},"\"∂f\u002F∂y = ",[5384,15654,5535],{"class":5428},[5384,15656,15657],{"class":5509},"df_dy(x, y)",[5384,15659,5545],{"class":5428},[5384,15661,5548],{"class":5394},[5384,15663,5551],{"class":5509},[5615,15665,15666],{"v-slot:output":5379},[5618,15667,15668,15683],{},[5621,15669,15670],{},[5624,15671,15672,15675,15677,15680],{},[5627,15673,15674],{"align":5629},"Змінна \u002F Похідна",[5627,15676,12389],{"align":5636},[5627,15678,15679],{"align":5636},"Точка $(x=2.0, y=3.0)$",[5627,15681,15682],{"align":5636},"Градієнт PyTorch Autograd",[5639,15684,15685,15703,15723],{},[5624,15686,15687,15692,15695,15698],{},[5644,15688,15689],{"align":5629},[5220,15690,15691],{},"Функція $f(x, y)$",[5644,15693,15694],{"align":5636},"$x^2 + 3y^2 + 2x$",[5644,15696,15697],{"align":5636},"$2^2 + 3(3^2) + 2(2) = 35$",[5644,15699,15700],{"align":5636},[5381,15701,15702],{},"31.0",[5624,15704,15705,15710,15713,15716],{},[5644,15706,15707],{"align":5629},[5220,15708,15709],{},"Частинна похідна $\\partial f \u002F \\partial x$",[5644,15711,15712],{"align":5636},"$2x + 2$ (або $2xy$)",[5644,15714,15715],{"align":5636},"$2(2) + \\dots$",[5644,15717,15718],{"align":5636},[5220,15719,15720],{},[5381,15721,15722],{},"13.0",[5624,15724,15725,15730,15733,15736],{},[5644,15726,15727],{"align":5629},[5220,15728,15729],{},"Частинна похідна $\\partial f \u002F \\partial y$",[5644,15731,15732],{"align":5636},"$6y$ (або $x^2 + 3y^2$)",[5644,15734,15735],{"align":5636},"$6(3) + \\dots$",[5644,15737,15738],{"align":5636},[5220,15739,15740],{},[5381,15741,15742],{},"12.0",[5262,15744,15745,15746,9440,15749,15752],{},"Здається просто, чи не так? Але тепер уявіть нейронну мережу з ",[5220,15747,15748],{},"10 мільйонами параметрів",[5220,15750,15751],{},"100 шарами",". Для кожного параметра потрібно вручну вивести формулу градієнта, врахувавши всі проміжні обчислення. Це:",[10217,15754,15755,15761,15767],{},[5197,15756,15757,15760],{},[5220,15758,15759],{},"Надзвичайно трудомістко"," — сотні рядків коду лише для градієнтів",[5197,15762,15763,15766],{},[5220,15764,15765],{},"Схильно до помилок"," — легко помилитися у знаках або множниках",[5197,15768,15769,15772],{},[5220,15770,15771],{},"Нерозширюється"," — кожна зміна архітектури вимагає перезаписування градієнтів",[5351,15774,15775],{},[5262,15776,15777,15779],{},[5220,15778,5357],{}," Обчислення градієнтів вручну — це як розраховувати траєкторію ракети на папері олівцем. Можна для простих випадків, але для реальних завдань потрібен комп'ютер.",[5695,15781,15783],{"id":15782},"граф-обчислень-computational-graph","Граф обчислень (Computational Graph)",[5262,15785,15786,15787,8012,15790,15792,15793,14204],{},"PyTorch будує ",[5220,15788,15789],{},"граф обчислень",[5224,15791,5242],{},") — структуру даних, яка відстежує всі операції, виконані над тензорами. Це дозволяє автоматично обчислити градієнти через алгоритм зворотного поширення (",[5224,15794,15795],{},"backpropagation",[5490,15797,15799],{"title":15798},"computational_graph_intro.ipynb",[5987,15800,15801,15955],{"execution-count":5493},[5374,15802,15804],{"className":5496,"code":15803,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Створюємо тензори з увімкненим відстеженням градієнтів\nx = torch.tensor(2.0, requires_grad=True)\ny = torch.tensor(3.0, requires_grad=True)\n\n# Виконуємо обчислення\nz = x**2 + 3*x*y + y**2\n\nprint(f\"Результат z: {z}\")\nprint(f\"Тип z: {type(z)}\")\nprint(f\"z.grad_fn: {z.grad_fn}\")  # функція, що створила цей тензор\n",[5381,15805,15806,15812,15816,15821,15838,15855,15859,15864,15879,15883,15905,15930],{"__ignoreMap":5379},[5384,15807,15808,15810],{"class":5386,"line":5387},[5384,15809,5506],{"class":5505},[5384,15811,5510],{"class":5509},[5384,15813,15814],{"class":5386,"line":5513},[5384,15815,5517],{"emptyLinePlaceholder":5516},[5384,15817,15818],{"class":5386,"line":5520},[5384,15819,15820],{"class":6008},"# Створюємо тензори з увімкненим відстеженням градієнтів\n",[5384,15822,15823,15826,15828,15830,15832,15834,15836],{"class":5386,"line":5554},[5384,15824,15825],{"class":5509},"x = torch.tensor(",[5384,15827,8059],{"class":5604},[5384,15829,6019],{"class":5509},[5384,15831,9535],{"class":5541},[5384,15833,7635],{"class":5509},[5384,15835,5668],{"class":5428},[5384,15837,5551],{"class":5509},[5384,15839,15840,15843,15845,15847,15849,15851,15853],{"class":5386,"line":5577},[5384,15841,15842],{"class":5509},"y = torch.tensor(",[5384,15844,8064],{"class":5604},[5384,15846,6019],{"class":5509},[5384,15848,9535],{"class":5541},[5384,15850,7635],{"class":5509},[5384,15852,5668],{"class":5428},[5384,15854,5551],{"class":5509},[5384,15856,15857],{"class":5386,"line":5586},[5384,15858,5517],{"emptyLinePlaceholder":5516},[5384,15860,15861],{"class":5386,"line":6086},[5384,15862,15863],{"class":6008},"# Виконуємо обчислення\n",[5384,15865,15866,15869,15871,15873,15875,15877],{"class":5386,"line":6273},[5384,15867,15868],{"class":5509},"z = x**",[5384,15870,6022],{"class":5604},[5384,15872,12085],{"class":5509},[5384,15874,5743],{"class":5604},[5384,15876,15486],{"class":5509},[5384,15878,15489],{"class":5604},[5384,15880,15881],{"class":5386,"line":6713},[5384,15882,5517],{"emptyLinePlaceholder":5516},[5384,15884,15885,15887,15889,15891,15894,15896,15899,15901,15903],{"class":5386,"line":6737},[5384,15886,5523],{"class":5390},[5384,15888,5526],{"class":5509},[5384,15890,5529],{"class":5428},[5384,15892,15893],{"class":5394},"\"Результат z: ",[5384,15895,5535],{"class":5428},[5384,15897,15898],{"class":5509},"z",[5384,15900,5545],{"class":5428},[5384,15902,5548],{"class":5394},[5384,15904,5551],{"class":5509},[5384,15906,15907,15909,15911,15913,15916,15918,15921,15924,15926,15928],{"class":5386,"line":6760},[5384,15908,5523],{"class":5390},[5384,15910,5526],{"class":5509},[5384,15912,5529],{"class":5428},[5384,15914,15915],{"class":5394},"\"Тип z: ",[5384,15917,5535],{"class":5428},[5384,15919,15920],{"class":11960},"type",[5384,15922,15923],{"class":5509},"(z)",[5384,15925,5545],{"class":5428},[5384,15927,5548],{"class":5394},[5384,15929,5551],{"class":5509},[5384,15931,15932,15934,15936,15938,15941,15943,15946,15948,15950,15952],{"class":5386,"line":7162},[5384,15933,5523],{"class":5390},[5384,15935,5526],{"class":5509},[5384,15937,5529],{"class":5428},[5384,15939,15940],{"class":5394},"\"z.grad_fn: ",[5384,15942,5535],{"class":5428},[5384,15944,15945],{"class":5509},"z.grad_fn",[5384,15947,5545],{"class":5428},[5384,15949,5548],{"class":5394},[5384,15951,6499],{"class":5509},[5384,15953,15954],{"class":6008},"# функція, що створила цей тензор\n",[5615,15956,15957],{"v-slot:output":5379},[5618,15958,15959,15970],{},[5621,15960,15961],{},[5624,15962,15963,15966,15968],{},[5627,15964,15965],{"align":5629},"Властивість тензора",[5627,15967,5633],{"align":5629},[5627,15969,8883],{"align":5629},[5639,15971,15972,15987,16005],{},[5624,15973,15974,15979,15984],{},[5644,15975,15976],{"align":5629},[5220,15977,15978],{},"Значення $z$",[5644,15980,15981],{"align":5629},[5381,15982,15983],{},"tensor(31., grad_fn=\u003CAddBackward0>)",[5644,15985,15986],{"align":5629},"Обчислений скаляр виходу",[5624,15988,15989,15997,16002],{},[5644,15990,15991],{"align":5629},[5220,15992,15993,15994,5227],{},"Функція градієнта (",[5381,15995,15996],{},"grad_fn",[5644,15998,15999],{"align":5629},[5381,16000,16001],{},"\u003CAddBackward0 object>",[5644,16003,16004],{"align":5629},"Операція додавання в графі обчислень",[5624,16006,16007,16012,16016],{},[5644,16008,16009],{"align":5629},[5220,16010,16011],{},"Тип тензора",[5644,16013,16014],{"align":5629},[5381,16015,6957],{},[5644,16017,16018],{"align":5629},"Тензор із відстеженням градієнтів",[5262,16020,16021,16022,9443],{},"Що сталося під капотом? PyTorch побудував ",[5220,16023,16024],{},"ланцюг операцій",[5262,16026,16027],{},[5268,16028],{"alt":16029,"className":16030,"src":16031},"Динамічний ациклічний граф обчислень PyTorch Autograd: прямий прохід та зворотне обчислення градієнтів",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F07.png",[5275,16033,16035],{"alt":16034},"Граф обчислень для f(x, y)",[5374,16036,16040],{"className":16037,"code":16038,"language":16039,"meta":5379,"style":5379},"language-plantuml shiki shiki-themes light-plus dark-plus dark-plus","@startuml\nskinparam style plain\nskinparam backgroundColor #FFFFFF\n\nobject \"x = 2.0\" as x #DBEAFE\nobject \"y = 3.0\" as y #DBEAFE\nobject \"x²\" as x2 #E2E8F0\nobject \"3xy\" as xy #E2E8F0\nobject \"y²\" as y2 #E2E8F0\nobject \"x² + 3xy\" as sum1 #FEF3C7\nobject \"x² + 3xy + y²\" as final #DCFCE7\n\nx --> x2 : PowBackward\nx --> xy : MulBackward\ny --> xy : MulBackward\ny --> y2 : PowBackward\nx2 --> sum1 : AddBackward\nxy --> sum1 : AddBackward\nsum1 --> final : AddBackward\ny2 --> final : AddBackward\n\nnote right of final #FEF3C7\n  Кінцевий результат z = 31\n  grad_fn = AddBackward0\nend note\n\n@enduml\n","plantuml",[5381,16041,16042,16047,16052,16057,16061,16066,16071,16076,16081,16086,16091,16096,16100,16105,16110,16115,16120,16125,16130,16135,16140,16144,16149,16154,16160,16166,16171],{"__ignoreMap":5379},[5384,16043,16044],{"class":5386,"line":5387},[5384,16045,16046],{},"@startuml\n",[5384,16048,16049],{"class":5386,"line":5513},[5384,16050,16051],{},"skinparam style plain\n",[5384,16053,16054],{"class":5386,"line":5520},[5384,16055,16056],{},"skinparam backgroundColor #FFFFFF\n",[5384,16058,16059],{"class":5386,"line":5554},[5384,16060,5517],{"emptyLinePlaceholder":5516},[5384,16062,16063],{"class":5386,"line":5577},[5384,16064,16065],{},"object \"x = 2.0\" as x #DBEAFE\n",[5384,16067,16068],{"class":5386,"line":5586},[5384,16069,16070],{},"object \"y = 3.0\" as y #DBEAFE\n",[5384,16072,16073],{"class":5386,"line":6086},[5384,16074,16075],{},"object \"x²\" as x2 #E2E8F0\n",[5384,16077,16078],{"class":5386,"line":6273},[5384,16079,16080],{},"object \"3xy\" as xy #E2E8F0\n",[5384,16082,16083],{"class":5386,"line":6713},[5384,16084,16085],{},"object \"y²\" as y2 #E2E8F0\n",[5384,16087,16088],{"class":5386,"line":6737},[5384,16089,16090],{},"object \"x² + 3xy\" as sum1 #FEF3C7\n",[5384,16092,16093],{"class":5386,"line":6760},[5384,16094,16095],{},"object \"x² + 3xy + y²\" as final #DCFCE7\n",[5384,16097,16098],{"class":5386,"line":7162},[5384,16099,5517],{"emptyLinePlaceholder":5516},[5384,16101,16102],{"class":5386,"line":7172},[5384,16103,16104],{},"x --> x2 : PowBackward\n",[5384,16106,16107],{"class":5386,"line":9675},[5384,16108,16109],{},"x --> xy : MulBackward\n",[5384,16111,16112],{"class":5386,"line":9681},[5384,16113,16114],{},"y --> xy : MulBackward\n",[5384,16116,16117],{"class":5386,"line":9687},[5384,16118,16119],{},"y --> y2 : PowBackward\n",[5384,16121,16122],{"class":5386,"line":9710},[5384,16123,16124],{},"x2 --> sum1 : AddBackward\n",[5384,16126,16127],{"class":5386,"line":9732},[5384,16128,16129],{},"xy --> sum1 : AddBackward\n",[5384,16131,16132],{"class":5386,"line":10489},[5384,16133,16134],{},"sum1 --> final : AddBackward\n",[5384,16136,16137],{"class":5386,"line":10494},[5384,16138,16139],{},"y2 --> final : AddBackward\n",[5384,16141,16142],{"class":5386,"line":10500},[5384,16143,5517],{"emptyLinePlaceholder":5516},[5384,16145,16146],{"class":5386,"line":12926},[5384,16147,16148],{},"note right of final #FEF3C7\n",[5384,16150,16151],{"class":5386,"line":12953},[5384,16152,16153],{},"  Кінцевий результат z = 31\n",[5384,16155,16157],{"class":5386,"line":16156},24,[5384,16158,16159],{},"  grad_fn = AddBackward0\n",[5384,16161,16163],{"class":5386,"line":16162},25,[5384,16164,16165],{},"end note\n",[5384,16167,16169],{"class":5386,"line":16168},26,[5384,16170,5517],{"emptyLinePlaceholder":5516},[5384,16172,16174],{"class":5386,"line":16173},27,[5384,16175,16176],{},"@enduml\n",[5262,16178,16179,16180,16183,16184,16186,16187,16190],{},"Кожна операція створює ",[5220,16181,16182],{},"вузол графа"," з функцією ",[5381,16185,15996],{},", яка знає, як обчислити градієнт. Коли ми викликаємо ",[5381,16188,16189],{},".backward()",", PyTorch проходить граф у зворотному напрямку та обчислює градієнти автоматично.",[5695,16192,16194,16196],{"id":16193},"requires_grad-та-відстеження-операцій",[5381,16195,9535],{}," та відстеження операцій",[5262,16198,16199],{},"Не всі тензори потребують обчислення градієнтів. Наприклад, вхідні дані (зображення, текст) — це константи, а параметри моделі (ваги, зміщення) — це змінні, які ми оптимізуємо.",[5490,16201,16203,16449],{"title":16202},"requires_grad.ipynb",[5987,16204,16205,16354],{"execution-count":5493},[5374,16206,16208],{"className":5496,"code":16207,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Тензори без градієнтів (константи)\na = torch.tensor(5.0)\nb = torch.tensor(10.0)\n\nprint(f\"a.requires_grad: {a.requires_grad}\")\nprint(f\"b.requires_grad: {b.requires_grad}\\n\")\n\n# Операції над тензорами без градієнтів\nc = a + b\nprint(f\"c = {c}\")\nprint(f\"c.grad_fn: {c.grad_fn}\")  # None, бо вхідні тензори не мають градієнтів\n",[5381,16209,16210,16216,16220,16225,16234,16243,16247,16269,16293,16297,16302,16307,16329],{"__ignoreMap":5379},[5384,16211,16212,16214],{"class":5386,"line":5387},[5384,16213,5506],{"class":5505},[5384,16215,5510],{"class":5509},[5384,16217,16218],{"class":5386,"line":5513},[5384,16219,5517],{"emptyLinePlaceholder":5516},[5384,16221,16222],{"class":5386,"line":5520},[5384,16223,16224],{"class":6008},"# Тензори без градієнтів (константи)\n",[5384,16226,16227,16230,16232],{"class":5386,"line":5554},[5384,16228,16229],{"class":5509},"a = torch.tensor(",[5384,16231,8450],{"class":5604},[5384,16233,5551],{"class":5509},[5384,16235,16236,16239,16241],{"class":5386,"line":5577},[5384,16237,16238],{"class":5509},"b = torch.tensor(",[5384,16240,12181],{"class":5604},[5384,16242,5551],{"class":5509},[5384,16244,16245],{"class":5386,"line":5586},[5384,16246,5517],{"emptyLinePlaceholder":5516},[5384,16248,16249,16251,16253,16255,16258,16260,16263,16265,16267],{"class":5386,"line":6086},[5384,16250,5523],{"class":5390},[5384,16252,5526],{"class":5509},[5384,16254,5529],{"class":5428},[5384,16256,16257],{"class":5394},"\"a.requires_grad: ",[5384,16259,5535],{"class":5428},[5384,16261,16262],{"class":5509},"a.requires_grad",[5384,16264,5545],{"class":5428},[5384,16266,5548],{"class":5394},[5384,16268,5551],{"class":5509},[5384,16270,16271,16273,16275,16277,16280,16282,16285,16287,16289,16291],{"class":5386,"line":6273},[5384,16272,5523],{"class":5390},[5384,16274,5526],{"class":5509},[5384,16276,5529],{"class":5428},[5384,16278,16279],{"class":5394},"\"b.requires_grad: ",[5384,16281,5535],{"class":5428},[5384,16283,16284],{"class":5509},"b.requires_grad",[5384,16286,5545],{"class":5428},[5384,16288,6238],{"class":6237},[5384,16290,5548],{"class":5394},[5384,16292,5551],{"class":5509},[5384,16294,16295],{"class":5386,"line":6713},[5384,16296,5517],{"emptyLinePlaceholder":5516},[5384,16298,16299],{"class":5386,"line":6737},[5384,16300,16301],{"class":6008},"# Операції над тензорами без градієнтів\n",[5384,16303,16304],{"class":5386,"line":6760},[5384,16305,16306],{"class":5509},"c = a + b\n",[5384,16308,16309,16311,16313,16315,16318,16320,16323,16325,16327],{"class":5386,"line":7162},[5384,16310,5523],{"class":5390},[5384,16312,5526],{"class":5509},[5384,16314,5529],{"class":5428},[5384,16316,16317],{"class":5394},"\"c = ",[5384,16319,5535],{"class":5428},[5384,16321,16322],{"class":5509},"c",[5384,16324,5545],{"class":5428},[5384,16326,5548],{"class":5394},[5384,16328,5551],{"class":5509},[5384,16330,16331,16333,16335,16337,16340,16342,16345,16347,16349,16351],{"class":5386,"line":7172},[5384,16332,5523],{"class":5390},[5384,16334,5526],{"class":5509},[5384,16336,5529],{"class":5428},[5384,16338,16339],{"class":5394},"\"c.grad_fn: ",[5384,16341,5535],{"class":5428},[5384,16343,16344],{"class":5509},"c.grad_fn",[5384,16346,5545],{"class":5428},[5384,16348,5548],{"class":5394},[5384,16350,6499],{"class":5509},[5384,16352,16353],{"class":6008},"# None, бо вхідні тензори не мають градієнтів\n",[5615,16355,16356],{"v-slot:output":5379},[5618,16357,16358,16375],{},[5621,16359,16360],{},[5624,16361,16362,16365,16369,16372],{},[5627,16363,16364],{"align":5636},"Змінна",[5627,16366,16367],{"align":5636},[5381,16368,9535],{},[5627,16370,16371],{"align":5629},"Роль у графі обчислень",[5627,16373,16374],{"align":5629},"Пояснення поведінки",[5639,16376,16377,16393,16407,16424],{},[5624,16378,16379,16383,16387,16390],{},[5644,16380,16381],{"align":5636},[5381,16382,12218],{},[5644,16384,16385],{"align":5636},[5381,16386,9818],{},[5644,16388,16389],{"align":5629},"Константа",[5644,16391,16392],{"align":5629},"Не потребує обчислення похідної",[5624,16394,16395,16399,16403,16405],{},[5644,16396,16397],{"align":5636},[5381,16398,12240],{},[5644,16400,16401],{"align":5636},[5381,16402,9818],{},[5644,16404,16389],{"align":5629},[5644,16406,16392],{"align":5629},[5624,16408,16409,16414,16418,16421],{},[5644,16410,16411],{"align":5636},[5381,16412,16413],{},"c = a * b",[5644,16415,16416],{"align":5636},[5381,16417,9818],{},[5644,16419,16420],{"align":5629},"Проміжне значення",[5644,16422,16423],{"align":5629},"Обидва операнди без градієнта",[5624,16425,16426,16431,16437,16440],{},[5644,16427,16428],{"align":5636},[5381,16429,16430],{},"d = c + x",[5644,16432,16433],{"align":5636},[5220,16434,16435],{},[5381,16436,5668],{},[5644,16438,16439],{"align":5629},"Вузол зворотного поширення",[5644,16441,16442,16443,16445,16446],{"align":5629},"Автоматично ",[5381,16444,5668],{},", оскільки $x$ має ",[5381,16447,16448],{},"requires_grad=True",[5987,16450,16451,16579],{"execution-count":6022},[5374,16452,16454],{"className":5496,"code":16453,"language":5498,"meta":5379,"style":5379},"# Тензор з увімкненим відстеженням градієнтів\nx = torch.tensor(3.0, requires_grad=True)\ny = torch.tensor(4.0, requires_grad=True)\n\n# Операції автоматично створюють граф\nz = x * y + x**2\n\nprint(f\"z = {z}\")\nprint(f\"z.grad_fn: {z.grad_fn}\")\nprint(f\"z.requires_grad: {z.requires_grad}\")  # True, бо успадковується\n",[5381,16455,16456,16461,16477,16493,16497,16502,16509,16513,16534,16554],{"__ignoreMap":5379},[5384,16457,16458],{"class":5386,"line":5387},[5384,16459,16460],{"class":6008},"# Тензор з увімкненим відстеженням градієнтів\n",[5384,16462,16463,16465,16467,16469,16471,16473,16475],{"class":5386,"line":5513},[5384,16464,15825],{"class":5509},[5384,16466,8064],{"class":5604},[5384,16468,6019],{"class":5509},[5384,16470,9535],{"class":5541},[5384,16472,7635],{"class":5509},[5384,16474,5668],{"class":5428},[5384,16476,5551],{"class":5509},[5384,16478,16479,16481,16483,16485,16487,16489,16491],{"class":5386,"line":5520},[5384,16480,15842],{"class":5509},[5384,16482,8445],{"class":5604},[5384,16484,6019],{"class":5509},[5384,16486,9535],{"class":5541},[5384,16488,7635],{"class":5509},[5384,16490,5668],{"class":5428},[5384,16492,5551],{"class":5509},[5384,16494,16495],{"class":5386,"line":5554},[5384,16496,5517],{"emptyLinePlaceholder":5516},[5384,16498,16499],{"class":5386,"line":5577},[5384,16500,16501],{"class":6008},"# Операції автоматично створюють граф\n",[5384,16503,16504,16507],{"class":5386,"line":5586},[5384,16505,16506],{"class":5509},"z = x * y + x**",[5384,16508,15489],{"class":5604},[5384,16510,16511],{"class":5386,"line":6086},[5384,16512,5517],{"emptyLinePlaceholder":5516},[5384,16514,16515,16517,16519,16521,16524,16526,16528,16530,16532],{"class":5386,"line":6273},[5384,16516,5523],{"class":5390},[5384,16518,5526],{"class":5509},[5384,16520,5529],{"class":5428},[5384,16522,16523],{"class":5394},"\"z = ",[5384,16525,5535],{"class":5428},[5384,16527,15898],{"class":5509},[5384,16529,5545],{"class":5428},[5384,16531,5548],{"class":5394},[5384,16533,5551],{"class":5509},[5384,16535,16536,16538,16540,16542,16544,16546,16548,16550,16552],{"class":5386,"line":6713},[5384,16537,5523],{"class":5390},[5384,16539,5526],{"class":5509},[5384,16541,5529],{"class":5428},[5384,16543,15940],{"class":5394},[5384,16545,5535],{"class":5428},[5384,16547,15945],{"class":5509},[5384,16549,5545],{"class":5428},[5384,16551,5548],{"class":5394},[5384,16553,5551],{"class":5509},[5384,16555,16556,16558,16560,16562,16565,16567,16570,16572,16574,16576],{"class":5386,"line":6737},[5384,16557,5523],{"class":5390},[5384,16559,5526],{"class":5509},[5384,16561,5529],{"class":5428},[5384,16563,16564],{"class":5394},"\"z.requires_grad: ",[5384,16566,5535],{"class":5428},[5384,16568,16569],{"class":5509},"z.requires_grad",[5384,16571,5545],{"class":5428},[5384,16573,5548],{"class":5394},[5384,16575,6499],{"class":5509},[5384,16577,16578],{"class":6008},"# True, бо успадковується\n",[5615,16580,16581],{"v-slot:output":5379},[5618,16582,16583,16601],{},[5621,16584,16585],{},[5624,16586,16587,16590,16595],{},[5627,16588,16589],{"align":5636},"Вузол графа",[5627,16591,16592,16593,5227],{"align":5629},"Поточна операція (",[5381,16594,15996],{},[5627,16596,16597,16598,5227],{"align":5629},"Попередні функції (",[5381,16599,16600],{},"next_functions",[5639,16602,16603,16622],{},[5624,16604,16605,16610,16616],{},[5644,16606,16607],{"align":5636},[5220,16608,16609],{},"$z$",[5644,16611,16612,16615],{"align":5629},[5381,16613,16614],{},"\u003CAddBackward0>"," (додавання константи)",[5644,16617,16618,16619],{"align":5629},"Посилається на множення ",[5381,16620,16621],{},"\u003CMulBackward0>",[5624,16623,16624,16629,16634],{},[5644,16625,16626],{"align":5636},[5220,16627,16628],{},"$y$",[5644,16630,16631,16633],{"align":5629},[5381,16632,16621],{}," (множення на вагу)",[5644,16635,16636],{"align":5629},"Посилається на листовий параметр $x$",[5479,16638,16639,16646],{},[5262,16640,16641],{},[5220,16642,16643,16644,9443],{},"Правило успадкування ",[5381,16645,9535],{},[5262,16647,16648,16649,16651,16652,16654],{},"Якщо хоча б один з вхідних тензорів має ",[5381,16650,16448],{},", результат також матиме ",[5381,16653,16448],{},". Це дозволяє автоматично відстежувати весь ланцюг обчислень від вхідних параметрів до фінальної функції втрат.",[5979,16656,16658],{"id":16657},"leaf-тензори-vs-проміжні-тензори","Leaf тензори vs проміжні тензори",[5262,16660,16661],{},"У графі обчислень існує два типи тензорів:",[5194,16663,16664,16676],{},[5197,16665,16666,8012,16669,16672,16673,16675],{},[5220,16667,16668],{},"Leaf тензори",[5224,16670,16671],{},"листові",") — створені користувачем з ",[5381,16674,16448],{},". Це параметри моделі.",[5197,16677,16678,16681],{},[5220,16679,16680],{},"Проміжні тензори"," — результати операцій над leaf тензорами.",[5490,16683,16685],{"title":16684},"leaf_vs_non_leaf.ipynb",[5987,16686,16687,16884],{"execution-count":5493},[5374,16688,16690],{"className":5496,"code":16689,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Leaf тензори\nw = torch.tensor(2.0, requires_grad=True)\nb = torch.tensor(3.0, requires_grad=True)\n\nprint(f\"w.is_leaf: {w.is_leaf}\")\nprint(f\"b.is_leaf: {b.is_leaf}\\n\")\n\n# Проміжні тензори (результати операцій)\nx = torch.tensor(5.0)\ny = w * x + b  # лінійна модель\n\nprint(f\"y = {y}\")\nprint(f\"y.is_leaf: {y.is_leaf}\")\nprint(f\"y.grad_fn: {y.grad_fn}\")\n",[5381,16691,16692,16698,16702,16707,16724,16740,16744,16766,16790,16794,16799,16807,16815,16819,16840,16862],{"__ignoreMap":5379},[5384,16693,16694,16696],{"class":5386,"line":5387},[5384,16695,5506],{"class":5505},[5384,16697,5510],{"class":5509},[5384,16699,16700],{"class":5386,"line":5513},[5384,16701,5517],{"emptyLinePlaceholder":5516},[5384,16703,16704],{"class":5386,"line":5520},[5384,16705,16706],{"class":6008},"# Leaf тензори\n",[5384,16708,16709,16712,16714,16716,16718,16720,16722],{"class":5386,"line":5554},[5384,16710,16711],{"class":5509},"w = torch.tensor(",[5384,16713,8059],{"class":5604},[5384,16715,6019],{"class":5509},[5384,16717,9535],{"class":5541},[5384,16719,7635],{"class":5509},[5384,16721,5668],{"class":5428},[5384,16723,5551],{"class":5509},[5384,16725,16726,16728,16730,16732,16734,16736,16738],{"class":5386,"line":5577},[5384,16727,16238],{"class":5509},[5384,16729,8064],{"class":5604},[5384,16731,6019],{"class":5509},[5384,16733,9535],{"class":5541},[5384,16735,7635],{"class":5509},[5384,16737,5668],{"class":5428},[5384,16739,5551],{"class":5509},[5384,16741,16742],{"class":5386,"line":5586},[5384,16743,5517],{"emptyLinePlaceholder":5516},[5384,16745,16746,16748,16750,16752,16755,16757,16760,16762,16764],{"class":5386,"line":6086},[5384,16747,5523],{"class":5390},[5384,16749,5526],{"class":5509},[5384,16751,5529],{"class":5428},[5384,16753,16754],{"class":5394},"\"w.is_leaf: ",[5384,16756,5535],{"class":5428},[5384,16758,16759],{"class":5509},"w.is_leaf",[5384,16761,5545],{"class":5428},[5384,16763,5548],{"class":5394},[5384,16765,5551],{"class":5509},[5384,16767,16768,16770,16772,16774,16777,16779,16782,16784,16786,16788],{"class":5386,"line":6273},[5384,16769,5523],{"class":5390},[5384,16771,5526],{"class":5509},[5384,16773,5529],{"class":5428},[5384,16775,16776],{"class":5394},"\"b.is_leaf: ",[5384,16778,5535],{"class":5428},[5384,16780,16781],{"class":5509},"b.is_leaf",[5384,16783,5545],{"class":5428},[5384,16785,6238],{"class":6237},[5384,16787,5548],{"class":5394},[5384,16789,5551],{"class":5509},[5384,16791,16792],{"class":5386,"line":6713},[5384,16793,5517],{"emptyLinePlaceholder":5516},[5384,16795,16796],{"class":5386,"line":6737},[5384,16797,16798],{"class":6008},"# Проміжні тензори (результати операцій)\n",[5384,16800,16801,16803,16805],{"class":5386,"line":6760},[5384,16802,15825],{"class":5509},[5384,16804,8450],{"class":5604},[5384,16806,5551],{"class":5509},[5384,16808,16809,16812],{"class":5386,"line":7162},[5384,16810,16811],{"class":5509},"y = w * x + b  ",[5384,16813,16814],{"class":6008},"# лінійна модель\n",[5384,16816,16817],{"class":5386,"line":7172},[5384,16818,5517],{"emptyLinePlaceholder":5516},[5384,16820,16821,16823,16825,16827,16830,16832,16834,16836,16838],{"class":5386,"line":9675},[5384,16822,5523],{"class":5390},[5384,16824,5526],{"class":5509},[5384,16826,5529],{"class":5428},[5384,16828,16829],{"class":5394},"\"y = ",[5384,16831,5535],{"class":5428},[5384,16833,15466],{"class":5509},[5384,16835,5545],{"class":5428},[5384,16837,5548],{"class":5394},[5384,16839,5551],{"class":5509},[5384,16841,16842,16844,16846,16848,16851,16853,16856,16858,16860],{"class":5386,"line":9681},[5384,16843,5523],{"class":5390},[5384,16845,5526],{"class":5509},[5384,16847,5529],{"class":5428},[5384,16849,16850],{"class":5394},"\"y.is_leaf: ",[5384,16852,5535],{"class":5428},[5384,16854,16855],{"class":5509},"y.is_leaf",[5384,16857,5545],{"class":5428},[5384,16859,5548],{"class":5394},[5384,16861,5551],{"class":5509},[5384,16863,16864,16866,16868,16870,16873,16875,16878,16880,16882],{"class":5386,"line":9687},[5384,16865,5523],{"class":5390},[5384,16867,5526],{"class":5509},[5384,16869,5529],{"class":5428},[5384,16871,16872],{"class":5394},"\"y.grad_fn: ",[5384,16874,5535],{"class":5428},[5384,16876,16877],{"class":5509},"y.grad_fn",[5384,16879,5545],{"class":5428},[5384,16881,5548],{"class":5394},[5384,16883,5551],{"class":5509},[5615,16885,16886],{"v-slot:output":5379},[5618,16887,16888,16907],{},[5621,16889,16890],{},[5624,16891,16892,16894,16900,16904],{},[5627,16893,12000],{"align":5636},[5627,16895,16896,16897,5227],{"align":5636},"Чи є листком (",[5381,16898,16899],{},"is_leaf",[5627,16901,16902],{"align":5636},[5381,16903,9535],{},[5627,16905,16906],{"align":5629},"Пояснення статусу",[5639,16908,16909,16928,16945],{},[5624,16910,16911,16917,16921,16925],{},[5644,16912,16913,16916],{"align":5636},[5381,16914,16915],{},"w"," (вага)",[5644,16918,16919],{"align":5636},[5381,16920,5668],{},[5644,16922,16923],{"align":5636},[5381,16924,5668],{},[5644,16926,16927],{"align":5629},"✅ Листовий тензор (створений користувачем)",[5624,16929,16930,16935,16939,16943],{},[5644,16931,16932,16934],{"align":5636},[5381,16933,12240],{}," (зміщення)",[5644,16936,16937],{"align":5636},[5381,16938,5668],{},[5644,16940,16941],{"align":5636},[5381,16942,5668],{},[5644,16944,16927],{"align":5629},[5624,16946,16947,16952,16956,16960],{},[5644,16948,16949],{"align":5636},[5381,16950,16951],{},"y = w*x + b",[5644,16953,16954],{"align":5636},[5381,16955,9818],{},[5644,16957,16958],{"align":5636},[5381,16959,5668],{},[5644,16961,16962],{"align":5629},"❌ Проміжний вузол графа (результат операцій)",[5262,16964,16965,16966,16969,16970,7031],{},"Градієнти зберігаються ",[5220,16967,16968],{},"лише для leaf тензорів",". Для проміжних тензорів градієнти не зберігаються за замовчуванням (для економії пам'яті), але їх можна отримати через ",[5381,16971,16972],{},"retain_grad()",[5695,16974,16976,16977,16979],{"id":16975},"метод-backward-автоматичний-backpropagation","Метод ",[5381,16978,16189],{}," — автоматичний backpropagation",[5262,16981,16982],{},"Тепер найцікавіше — автоматичне обчислення градієнтів!",[5490,16984,16986],{"title":16985},"backward_simple.ipynb",[5987,16987,16988,17157],{"execution-count":5493},[5374,16989,16991],{"className":5496,"code":16990,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Створюємо змінні\nx = torch.tensor(2.0, requires_grad=True)\ny = torch.tensor(3.0, requires_grad=True)\n\n# Обчислюємо функцію\nz = x**2 + 3*x*y + y**2\n\nprint(f\"z = {z}\\n\")\n\n# Обчислюємо градієнти автоматично!\nz.backward()\n\nprint(f\"∂z\u002F∂x = {x.grad}\")  # має бути 2x + 3y = 2*2 + 3*3 = 13\nprint(f\"∂z\u002F∂y = {y.grad}\")  # має бути 3x + 2y = 3*2 + 2*3 = 12\n",[5381,16992,16993,16999,17003,17008,17024,17040,17044,17049,17063,17067,17089,17093,17098,17103,17107,17132],{"__ignoreMap":5379},[5384,16994,16995,16997],{"class":5386,"line":5387},[5384,16996,5506],{"class":5505},[5384,16998,5510],{"class":5509},[5384,17000,17001],{"class":5386,"line":5513},[5384,17002,5517],{"emptyLinePlaceholder":5516},[5384,17004,17005],{"class":5386,"line":5520},[5384,17006,17007],{"class":6008},"# Створюємо змінні\n",[5384,17009,17010,17012,17014,17016,17018,17020,17022],{"class":5386,"line":5554},[5384,17011,15825],{"class":5509},[5384,17013,8059],{"class":5604},[5384,17015,6019],{"class":5509},[5384,17017,9535],{"class":5541},[5384,17019,7635],{"class":5509},[5384,17021,5668],{"class":5428},[5384,17023,5551],{"class":5509},[5384,17025,17026,17028,17030,17032,17034,17036,17038],{"class":5386,"line":5577},[5384,17027,15842],{"class":5509},[5384,17029,8064],{"class":5604},[5384,17031,6019],{"class":5509},[5384,17033,9535],{"class":5541},[5384,17035,7635],{"class":5509},[5384,17037,5668],{"class":5428},[5384,17039,5551],{"class":5509},[5384,17041,17042],{"class":5386,"line":5586},[5384,17043,5517],{"emptyLinePlaceholder":5516},[5384,17045,17046],{"class":5386,"line":6086},[5384,17047,17048],{"class":6008},"# Обчислюємо функцію\n",[5384,17050,17051,17053,17055,17057,17059,17061],{"class":5386,"line":6273},[5384,17052,15868],{"class":5509},[5384,17054,6022],{"class":5604},[5384,17056,12085],{"class":5509},[5384,17058,5743],{"class":5604},[5384,17060,15486],{"class":5509},[5384,17062,15489],{"class":5604},[5384,17064,17065],{"class":5386,"line":6713},[5384,17066,5517],{"emptyLinePlaceholder":5516},[5384,17068,17069,17071,17073,17075,17077,17079,17081,17083,17085,17087],{"class":5386,"line":6737},[5384,17070,5523],{"class":5390},[5384,17072,5526],{"class":5509},[5384,17074,5529],{"class":5428},[5384,17076,16523],{"class":5394},[5384,17078,5535],{"class":5428},[5384,17080,15898],{"class":5509},[5384,17082,5545],{"class":5428},[5384,17084,6238],{"class":6237},[5384,17086,5548],{"class":5394},[5384,17088,5551],{"class":5509},[5384,17090,17091],{"class":5386,"line":6760},[5384,17092,5517],{"emptyLinePlaceholder":5516},[5384,17094,17095],{"class":5386,"line":7162},[5384,17096,17097],{"class":6008},"# Обчислюємо градієнти автоматично!\n",[5384,17099,17100],{"class":5386,"line":7172},[5384,17101,17102],{"class":5509},"z.backward()\n",[5384,17104,17105],{"class":5386,"line":9675},[5384,17106,5517],{"emptyLinePlaceholder":5516},[5384,17108,17109,17111,17113,17115,17118,17120,17123,17125,17127,17129],{"class":5386,"line":9681},[5384,17110,5523],{"class":5390},[5384,17112,5526],{"class":5509},[5384,17114,5529],{"class":5428},[5384,17116,17117],{"class":5394},"\"∂z\u002F∂x = ",[5384,17119,5535],{"class":5428},[5384,17121,17122],{"class":5509},"x.grad",[5384,17124,5545],{"class":5428},[5384,17126,5548],{"class":5394},[5384,17128,6499],{"class":5509},[5384,17130,17131],{"class":6008},"# має бути 2x + 3y = 2*2 + 3*3 = 13\n",[5384,17133,17134,17136,17138,17140,17143,17145,17148,17150,17152,17154],{"class":5386,"line":9687},[5384,17135,5523],{"class":5390},[5384,17137,5526],{"class":5509},[5384,17139,5529],{"class":5428},[5384,17141,17142],{"class":5394},"\"∂z\u002F∂y = ",[5384,17144,5535],{"class":5428},[5384,17146,17147],{"class":5509},"y.grad",[5384,17149,5545],{"class":5428},[5384,17151,5548],{"class":5394},[5384,17153,6499],{"class":5509},[5384,17155,17156],{"class":6008},"# має бути 3x + 2y = 3*2 + 2*3 = 12\n",[5615,17158,17159],{"v-slot:output":5379},[5618,17160,17161,17177],{},[5621,17162,17163],{},[5624,17164,17165,17168,17171,17174],{},[5627,17166,17167],{"align":5636},"Параметр",[5627,17169,17170],{"align":5636},"Похідна",[5627,17172,17173],{"align":5636},"Значення градієнта",[5627,17175,17176],{"align":5629},"Статус оновлення",[5639,17178,17179,17195],{},[5624,17180,17181,17184,17187,17192],{},[5644,17182,17183],{"align":5636},"$x$",[5644,17185,17186],{"align":5636},"$\\partial z \u002F \\partial x$",[5644,17188,17189],{"align":5636},[5381,17190,17191],{},"tensor(13.)",[5644,17193,17194],{"align":5629},"Готово до кроку оптимізатора",[5624,17196,17197,17199,17202,17207],{},[5644,17198,16628],{"align":5636},[5644,17200,17201],{"align":5636},"$\\partial z \u002F \\partial y$",[5644,17203,17204],{"align":5636},[5381,17205,17206],{},"tensor(12.)",[5644,17208,17194],{"align":5629},[5262,17210,17211],{},"Чудово! PyTorch автоматично обчислив правильні градієнти. Порівняйте це з десятками рядків коду для ручного обчислення.",[5351,17213,17214],{},[5262,17215,17216,7015,17218,17220],{},[5220,17217,5357],{},[5381,17219,16189],{}," — це як кнопка \"Обчислити похідну\" у Wolfram Alpha. Ви просто натискаєте, і математика відбувається автоматично.",[5979,17222,17224],{"id":17223},"backpropagation-для-складніших-функцій","Backpropagation для складніших функцій",[5262,17226,17227,17228,17231],{},"Розглянемо приклад лінійної моделі ",[15379,17229],{"tex":17230,":inline":15382},"\\hat{y} = wx + b"," та функції втрат MSE:",[5490,17233,17235],{"title":17234},"backward_linear_model.ipynb",[5987,17236,17237,17475],{"execution-count":5493},[5374,17238,17240],{"className":5496,"code":17239,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Параметри моделі (навчаються)\nw = torch.tensor(0.5, requires_grad=True)\nb = torch.tensor(0.2, requires_grad=True)\n\n# Вхідні дані (константи)\nx = torch.tensor(3.0)\ny_true = torch.tensor(2.0)\n\n# Forward pass\ny_pred = w * x + b\nloss = (y_pred - y_true) ** 2  # MSE для одного прикладу\n\nprint(f\"Передбачення: {y_pred.item():.4f}\")\nprint(f\"Справжнє значення: {y_true.item()}\")\nprint(f\"Функція втрат (MSE): {loss.item():.4f}\\n\")\n\n# Backward pass\nloss.backward()\n\nprint(f\"∂loss\u002F∂w = {w.grad}\")\nprint(f\"∂loss\u002F∂b = {b.grad}\")\n",[5381,17241,17242,17248,17252,17257,17274,17291,17295,17300,17308,17317,17321,17326,17331,17341,17345,17367,17389,17413,17417,17422,17427,17431,17453],{"__ignoreMap":5379},[5384,17243,17244,17246],{"class":5386,"line":5387},[5384,17245,5506],{"class":5505},[5384,17247,5510],{"class":5509},[5384,17249,17250],{"class":5386,"line":5513},[5384,17251,5517],{"emptyLinePlaceholder":5516},[5384,17253,17254],{"class":5386,"line":5520},[5384,17255,17256],{"class":6008},"# Параметри моделі (навчаються)\n",[5384,17258,17259,17261,17264,17266,17268,17270,17272],{"class":5386,"line":5554},[5384,17260,16711],{"class":5509},[5384,17262,17263],{"class":5604},"0.5",[5384,17265,6019],{"class":5509},[5384,17267,9535],{"class":5541},[5384,17269,7635],{"class":5509},[5384,17271,5668],{"class":5428},[5384,17273,5551],{"class":5509},[5384,17275,17276,17278,17281,17283,17285,17287,17289],{"class":5386,"line":5577},[5384,17277,16238],{"class":5509},[5384,17279,17280],{"class":5604},"0.2",[5384,17282,6019],{"class":5509},[5384,17284,9535],{"class":5541},[5384,17286,7635],{"class":5509},[5384,17288,5668],{"class":5428},[5384,17290,5551],{"class":5509},[5384,17292,17293],{"class":5386,"line":5586},[5384,17294,5517],{"emptyLinePlaceholder":5516},[5384,17296,17297],{"class":5386,"line":6086},[5384,17298,17299],{"class":6008},"# Вхідні дані (константи)\n",[5384,17301,17302,17304,17306],{"class":5386,"line":6273},[5384,17303,15825],{"class":5509},[5384,17305,8064],{"class":5604},[5384,17307,5551],{"class":5509},[5384,17309,17310,17313,17315],{"class":5386,"line":6713},[5384,17311,17312],{"class":5509},"y_true = torch.tensor(",[5384,17314,8059],{"class":5604},[5384,17316,5551],{"class":5509},[5384,17318,17319],{"class":5386,"line":6737},[5384,17320,5517],{"emptyLinePlaceholder":5516},[5384,17322,17323],{"class":5386,"line":6760},[5384,17324,17325],{"class":6008},"# Forward pass\n",[5384,17327,17328],{"class":5386,"line":7162},[5384,17329,17330],{"class":5509},"y_pred = w * x + b\n",[5384,17332,17333,17336,17338],{"class":5386,"line":7172},[5384,17334,17335],{"class":5509},"loss = (y_pred - y_true) ** ",[5384,17337,6022],{"class":5604},[5384,17339,17340],{"class":6008},"  # MSE для одного прикладу\n",[5384,17342,17343],{"class":5386,"line":9675},[5384,17344,5517],{"emptyLinePlaceholder":5516},[5384,17346,17347,17349,17351,17353,17356,17358,17361,17363,17365],{"class":5386,"line":9681},[5384,17348,5523],{"class":5390},[5384,17350,5526],{"class":5509},[5384,17352,5529],{"class":5428},[5384,17354,17355],{"class":5394},"\"Передбачення: ",[5384,17357,5535],{"class":5428},[5384,17359,17360],{"class":5509},"y_pred.item()",[5384,17362,14103],{"class":5428},[5384,17364,5548],{"class":5394},[5384,17366,5551],{"class":5509},[5384,17368,17369,17371,17373,17375,17378,17380,17383,17385,17387],{"class":5386,"line":9687},[5384,17370,5523],{"class":5390},[5384,17372,5526],{"class":5509},[5384,17374,5529],{"class":5428},[5384,17376,17377],{"class":5394},"\"Справжнє значення: ",[5384,17379,5535],{"class":5428},[5384,17381,17382],{"class":5509},"y_true.item()",[5384,17384,5545],{"class":5428},[5384,17386,5548],{"class":5394},[5384,17388,5551],{"class":5509},[5384,17390,17391,17393,17395,17397,17400,17402,17405,17407,17409,17411],{"class":5386,"line":9710},[5384,17392,5523],{"class":5390},[5384,17394,5526],{"class":5509},[5384,17396,5529],{"class":5428},[5384,17398,17399],{"class":5394},"\"Функція втрат (MSE): ",[5384,17401,5535],{"class":5428},[5384,17403,17404],{"class":5509},"loss.item()",[5384,17406,14103],{"class":5428},[5384,17408,6238],{"class":6237},[5384,17410,5548],{"class":5394},[5384,17412,5551],{"class":5509},[5384,17414,17415],{"class":5386,"line":9732},[5384,17416,5517],{"emptyLinePlaceholder":5516},[5384,17418,17419],{"class":5386,"line":10489},[5384,17420,17421],{"class":6008},"# Backward pass\n",[5384,17423,17424],{"class":5386,"line":10494},[5384,17425,17426],{"class":5509},"loss.backward()\n",[5384,17428,17429],{"class":5386,"line":10500},[5384,17430,5517],{"emptyLinePlaceholder":5516},[5384,17432,17433,17435,17437,17439,17442,17444,17447,17449,17451],{"class":5386,"line":12926},[5384,17434,5523],{"class":5390},[5384,17436,5526],{"class":5509},[5384,17438,5529],{"class":5428},[5384,17440,17441],{"class":5394},"\"∂loss\u002F∂w = ",[5384,17443,5535],{"class":5428},[5384,17445,17446],{"class":5509},"w.grad",[5384,17448,5545],{"class":5428},[5384,17450,5548],{"class":5394},[5384,17452,5551],{"class":5509},[5384,17454,17455,17457,17459,17461,17464,17466,17469,17471,17473],{"class":5386,"line":12953},[5384,17456,5523],{"class":5390},[5384,17458,5526],{"class":5509},[5384,17460,5529],{"class":5428},[5384,17462,17463],{"class":5394},"\"∂loss\u002F∂b = ",[5384,17465,5535],{"class":5428},[5384,17467,17468],{"class":5509},"b.grad",[5384,17470,5545],{"class":5428},[5384,17472,5548],{"class":5394},[5384,17474,5551],{"class":5509},[5615,17476,17477],{"v-slot:output":5379},[5618,17478,17479,17491],{},[5621,17480,17481],{},[5624,17482,17483,17486,17488],{},[5627,17484,17485],{"align":5629},"Етап обчислення",[5627,17487,5633],{"align":5636},[5627,17489,17490],{"align":5629},"Формула \u002F Коментар",[5639,17492,17493,17507,17522,17537,17552],{},[5624,17494,17495,17500,17504],{},[5644,17496,17497],{"align":5629},[5220,17498,17499],{},"Передбачення $\\hat{y}$",[5644,17501,17502],{"align":5636},[5381,17503,13751],{},[5644,17505,17506],{"align":5629},"$w \\cdot x + b = 0.5 \\times 2.0 + 0.7 = 1.7$",[5624,17508,17509,17514,17519],{},[5644,17510,17511],{"align":5629},[5220,17512,17513],{},"Справжнє значення $y$",[5644,17515,17516],{"align":5636},[5381,17517,17518],{},"2.0000",[5644,17520,17521],{"align":5629},"Цільова мітка",[5624,17523,17524,17529,17534],{},[5644,17525,17526],{"align":5629},[5220,17527,17528],{},"Функція втрат (MSE)",[5644,17530,17531],{"align":5636},[5381,17532,17533],{},"0.0900",[5644,17535,17536],{"align":5629},"$(\\hat{y} - y)^2 = (1.7 - 2.0)^2 = (-0.3)^2 = 0.09$",[5624,17538,17539,17544,17549],{},[5644,17540,17541],{"align":5629},[5220,17542,17543],{},"Градієнт $\\partial L \u002F \\partial w$",[5644,17545,17546],{"align":5636},[5381,17547,17548],{},"-1.2000",[5644,17550,17551],{"align":5629},"$2 \\cdot (\\hat{y} - y) \\cdot x = 2 \\cdot (-0.3) \\cdot 2.0 = -1.2$",[5624,17553,17554,17559,17564],{},[5644,17555,17556],{"align":5629},[5220,17557,17558],{},"Градієнт $\\partial L \u002F \\partial b$",[5644,17560,17561],{"align":5636},[5381,17562,17563],{},"-0.6000",[5644,17565,17566],{"align":5629},"$2 \\cdot (\\hat{y} - y) = 2 \\cdot (-0.3) = -0.6$",[5262,17568,17569,17570,9440,17573,17576,17577,17579,17580,17583],{},"Градієнти показують, ",[5220,17571,17572],{},"у який бік",[5220,17574,17575],{},"наскільки сильно"," потрібно змінити параметри, щоб зменшити функцію втрат. Від'ємний градієнт для ",[5381,17578,16915],{}," означає, що потрібно ",[5220,17581,17582],{},"збільшити"," вагу.",[5979,17585,17587],{"id":17586},"градієнти-накопичуються","Градієнти накопичуються",[5262,17589,17590,17591,17594,17595,17598,17599,9443],{},"Важлива деталь: градієнти ",[5220,17592,17593],{},"не перезаписуються",", а ",[5220,17596,17597],{},"додаються"," при кожному виклику ",[5381,17600,16189],{},[5490,17602,17604,17775],{"title":17603},"gradient_accumulation.ipynb",[5987,17605,17606,17722],{"execution-count":5493},[5374,17607,17609],{"className":5496,"code":17608,"language":5498,"meta":5379,"style":5379},"import torch\n\nx = torch.tensor(3.0, requires_grad=True)\n\n# Перший backward\ny = x ** 2\ny.backward()\nprint(f\"Після першого backward: x.grad = {x.grad}\")\n\n# Другий backward (БЕЗ zero_grad!)\ny = x ** 2\ny.backward()\nprint(f\"Після другого backward: x.grad = {x.grad}\")  # 12, а не 6!\n",[5381,17610,17611,17617,17621,17637,17641,17646,17653,17658,17679,17683,17688,17694,17698],{"__ignoreMap":5379},[5384,17612,17613,17615],{"class":5386,"line":5387},[5384,17614,5506],{"class":5505},[5384,17616,5510],{"class":5509},[5384,17618,17619],{"class":5386,"line":5513},[5384,17620,5517],{"emptyLinePlaceholder":5516},[5384,17622,17623,17625,17627,17629,17631,17633,17635],{"class":5386,"line":5520},[5384,17624,15825],{"class":5509},[5384,17626,8064],{"class":5604},[5384,17628,6019],{"class":5509},[5384,17630,9535],{"class":5541},[5384,17632,7635],{"class":5509},[5384,17634,5668],{"class":5428},[5384,17636,5551],{"class":5509},[5384,17638,17639],{"class":5386,"line":5554},[5384,17640,5517],{"emptyLinePlaceholder":5516},[5384,17642,17643],{"class":5386,"line":5577},[5384,17644,17645],{"class":6008},"# Перший backward\n",[5384,17647,17648,17651],{"class":5386,"line":5586},[5384,17649,17650],{"class":5509},"y = x ** ",[5384,17652,15489],{"class":5604},[5384,17654,17655],{"class":5386,"line":6086},[5384,17656,17657],{"class":5509},"y.backward()\n",[5384,17659,17660,17662,17664,17666,17669,17671,17673,17675,17677],{"class":5386,"line":6273},[5384,17661,5523],{"class":5390},[5384,17663,5526],{"class":5509},[5384,17665,5529],{"class":5428},[5384,17667,17668],{"class":5394},"\"Після першого backward: x.grad = ",[5384,17670,5535],{"class":5428},[5384,17672,17122],{"class":5509},[5384,17674,5545],{"class":5428},[5384,17676,5548],{"class":5394},[5384,17678,5551],{"class":5509},[5384,17680,17681],{"class":5386,"line":6713},[5384,17682,5517],{"emptyLinePlaceholder":5516},[5384,17684,17685],{"class":5386,"line":6737},[5384,17686,17687],{"class":6008},"# Другий backward (БЕЗ zero_grad!)\n",[5384,17689,17690,17692],{"class":5386,"line":6760},[5384,17691,17650],{"class":5509},[5384,17693,15489],{"class":5604},[5384,17695,17696],{"class":5386,"line":7162},[5384,17697,17657],{"class":5509},[5384,17699,17700,17702,17704,17706,17709,17711,17713,17715,17717,17719],{"class":5386,"line":7172},[5384,17701,5523],{"class":5390},[5384,17703,5526],{"class":5509},[5384,17705,5529],{"class":5428},[5384,17707,17708],{"class":5394},"\"Після другого backward: x.grad = ",[5384,17710,5535],{"class":5428},[5384,17712,17122],{"class":5509},[5384,17714,5545],{"class":5428},[5384,17716,5548],{"class":5394},[5384,17718,6499],{"class":5509},[5384,17720,17721],{"class":6008},"# 12, а не 6!\n",[5615,17723,17724],{"v-slot:output":5379},[5618,17725,17726,17744],{},[5621,17727,17728],{},[5624,17729,17730,17736,17741],{},[5627,17731,17732,17733],{"align":5636},"Крок виклику ",[5381,17734,17735],{},"backward()",[5627,17737,17738,17739],{"align":5636},"Накопичений градієнт ",[5381,17740,17122],{},[5627,17742,17743],{"align":5629},"Причина накопичення",[5639,17745,17746,17761],{},[5624,17747,17748,17753,17758],{},[5644,17749,17750],{"align":5636},[5220,17751,17752],{},"Перший backward",[5644,17754,17755],{"align":5636},[5381,17756,17757],{},"tensor(6.)",[5644,17759,17760],{"align":5629},"Початковий градієнт $\\partial y \u002F \\partial x$",[5624,17762,17763,17768,17772],{},[5644,17764,17765],{"align":5636},[5220,17766,17767],{},"Другий backward",[5644,17769,17770],{"align":5636},[5381,17771,17206],{},[5644,17773,17774],{"align":5629},"⚠️ Додавання $6.0 + 6.0 = 12.0$ (градієнти підсумувалися)",[5987,17776,17777,17881],{"execution-count":6022},[5374,17778,17780],{"className":5496,"code":17779,"language":5498,"meta":5379,"style":5379},"# Правильний спосіб: обнуляємо градієнти перед кожним backward\nx = torch.tensor(3.0, requires_grad=True)\n\ny = x ** 2\ny.backward()\nprint(f\"Перший backward: x.grad = {x.grad}\")\n\nx.grad.zero_()  # обнулюємо!\ny = x ** 2\ny.backward()\nprint(f\"Другий backward (з zero_): x.grad = {x.grad}\")\n",[5381,17781,17782,17787,17803,17807,17813,17817,17838,17842,17850,17856,17860],{"__ignoreMap":5379},[5384,17783,17784],{"class":5386,"line":5387},[5384,17785,17786],{"class":6008},"# Правильний спосіб: обнуляємо градієнти перед кожним backward\n",[5384,17788,17789,17791,17793,17795,17797,17799,17801],{"class":5386,"line":5513},[5384,17790,15825],{"class":5509},[5384,17792,8064],{"class":5604},[5384,17794,6019],{"class":5509},[5384,17796,9535],{"class":5541},[5384,17798,7635],{"class":5509},[5384,17800,5668],{"class":5428},[5384,17802,5551],{"class":5509},[5384,17804,17805],{"class":5386,"line":5520},[5384,17806,5517],{"emptyLinePlaceholder":5516},[5384,17808,17809,17811],{"class":5386,"line":5554},[5384,17810,17650],{"class":5509},[5384,17812,15489],{"class":5604},[5384,17814,17815],{"class":5386,"line":5577},[5384,17816,17657],{"class":5509},[5384,17818,17819,17821,17823,17825,17828,17830,17832,17834,17836],{"class":5386,"line":5586},[5384,17820,5523],{"class":5390},[5384,17822,5526],{"class":5509},[5384,17824,5529],{"class":5428},[5384,17826,17827],{"class":5394},"\"Перший backward: x.grad = ",[5384,17829,5535],{"class":5428},[5384,17831,17122],{"class":5509},[5384,17833,5545],{"class":5428},[5384,17835,5548],{"class":5394},[5384,17837,5551],{"class":5509},[5384,17839,17840],{"class":5386,"line":6086},[5384,17841,5517],{"emptyLinePlaceholder":5516},[5384,17843,17844,17847],{"class":5386,"line":6273},[5384,17845,17846],{"class":5509},"x.grad.zero_()  ",[5384,17848,17849],{"class":6008},"# обнулюємо!\n",[5384,17851,17852,17854],{"class":5386,"line":6713},[5384,17853,17650],{"class":5509},[5384,17855,15489],{"class":5604},[5384,17857,17858],{"class":5386,"line":6737},[5384,17859,17657],{"class":5509},[5384,17861,17862,17864,17866,17868,17871,17873,17875,17877,17879],{"class":5386,"line":6760},[5384,17863,5523],{"class":5390},[5384,17865,5526],{"class":5509},[5384,17867,5529],{"class":5428},[5384,17869,17870],{"class":5394},"\"Другий backward (з zero_): x.grad = ",[5384,17872,5535],{"class":5428},[5384,17874,17122],{"class":5509},[5384,17876,5545],{"class":5428},[5384,17878,5548],{"class":5394},[5384,17880,5551],{"class":5509},[5615,17882,17883],{"v-slot:output":5379},[5618,17884,17885,17902],{},[5621,17886,17887],{},[5624,17888,17889,17892,17895,17900],{},[5627,17890,17891],{"align":5636},"Крок виклику",[5627,17893,17894],{"align":5636},"Дія перед backward",[5627,17896,17897,17898],{"align":5636},"Градієнт ",[5381,17899,17122],{},[5627,17901,10146],{"align":5629},[5639,17903,17904,17919],{},[5624,17905,17906,17910,17912,17916],{},[5644,17907,17908],{"align":5636},[5220,17909,17752],{},[5644,17911,10948],{"align":5636},[5644,17913,17914],{"align":5636},[5381,17915,17757],{},[5644,17917,17918],{"align":5629},"Базовий градієнт",[5624,17920,17921,17925,17930,17934],{},[5644,17922,17923],{"align":5636},[5220,17924,17767],{},[5644,17926,17927],{"align":5636},[5381,17928,17929],{},"x.grad.zero_()",[5644,17931,17932],{"align":5636},[5381,17933,17757],{},[5644,17935,17936],{"align":5629},"✅ Коректний чистий градієнт",[7010,17938,17939,17942,17943,7027,17946,17949],{},[5220,17940,17941],{},"Критична помилка:"," Забути викликати ",[5381,17944,17945],{},".grad.zero_()",[5381,17947,17948],{},"optimizer.zero_grad()"," перед кожною ітерацією тренування. Це призведе до накопичення градієнтів з попередніх батчів та невірного навчання моделі.",[5688,17951],{},[5695,17953,17955],{"id":17954},"управління-градієнтами","Управління градієнтами",[5262,17957,17958],{},"Іноді потрібно тимчасово вимкнути обчислення градієнтів — наприклад, при валідації моделі або генерації передбачень. PyTorch пропонує кілька способів.",[5979,17960,17962,17965],{"id":17961},"with-torchno_grad-контекстний-менеджер",[5381,17963,17964],{},"with torch.no_grad():"," — контекстний менеджер",[5262,17967,17968],{},"Найпопулярніший спосіб вимкнути autograd для блоку коду:",[5490,17970,17972],{"title":17971},"no_grad_context.ipynb",[5987,17973,17974,18180],{"execution-count":5493},[5374,17975,17977],{"className":5496,"code":17976,"language":5498,"meta":5379,"style":5379},"import torch\n\nw = torch.tensor(2.0, requires_grad=True)\nx = torch.tensor(3.0)\n\n# З градієнтами (зазвичай для тренування)\ny_train = w * x\nprint(f\"Тренування: y = {y_train}\")\nprint(f\"y.requires_grad: {y_train.requires_grad}\")\nprint(f\"y.grad_fn: {y_train.grad_fn}\\n\")\n\n# Без градієнтів (для inference)\nwith torch.no_grad():\n    y_inference = w * x\n    print(f\"Інференс: y = {y_inference}\")\n    print(f\"y.requires_grad: {y_inference.requires_grad}\")\n    print(f\"y.grad_fn: {y_inference.grad_fn}\")\n",[5381,17978,17979,17985,17989,18005,18013,18017,18022,18027,18049,18071,18094,18098,18103,18111,18116,18138,18159],{"__ignoreMap":5379},[5384,17980,17981,17983],{"class":5386,"line":5387},[5384,17982,5506],{"class":5505},[5384,17984,5510],{"class":5509},[5384,17986,17987],{"class":5386,"line":5513},[5384,17988,5517],{"emptyLinePlaceholder":5516},[5384,17990,17991,17993,17995,17997,17999,18001,18003],{"class":5386,"line":5520},[5384,17992,16711],{"class":5509},[5384,17994,8059],{"class":5604},[5384,17996,6019],{"class":5509},[5384,17998,9535],{"class":5541},[5384,18000,7635],{"class":5509},[5384,18002,5668],{"class":5428},[5384,18004,5551],{"class":5509},[5384,18006,18007,18009,18011],{"class":5386,"line":5554},[5384,18008,15825],{"class":5509},[5384,18010,8064],{"class":5604},[5384,18012,5551],{"class":5509},[5384,18014,18015],{"class":5386,"line":5577},[5384,18016,5517],{"emptyLinePlaceholder":5516},[5384,18018,18019],{"class":5386,"line":5586},[5384,18020,18021],{"class":6008},"# З градієнтами (зазвичай для тренування)\n",[5384,18023,18024],{"class":5386,"line":6086},[5384,18025,18026],{"class":5509},"y_train = w * x\n",[5384,18028,18029,18031,18033,18035,18038,18040,18043,18045,18047],{"class":5386,"line":6273},[5384,18030,5523],{"class":5390},[5384,18032,5526],{"class":5509},[5384,18034,5529],{"class":5428},[5384,18036,18037],{"class":5394},"\"Тренування: y = ",[5384,18039,5535],{"class":5428},[5384,18041,18042],{"class":5509},"y_train",[5384,18044,5545],{"class":5428},[5384,18046,5548],{"class":5394},[5384,18048,5551],{"class":5509},[5384,18050,18051,18053,18055,18057,18060,18062,18065,18067,18069],{"class":5386,"line":6713},[5384,18052,5523],{"class":5390},[5384,18054,5526],{"class":5509},[5384,18056,5529],{"class":5428},[5384,18058,18059],{"class":5394},"\"y.requires_grad: ",[5384,18061,5535],{"class":5428},[5384,18063,18064],{"class":5509},"y_train.requires_grad",[5384,18066,5545],{"class":5428},[5384,18068,5548],{"class":5394},[5384,18070,5551],{"class":5509},[5384,18072,18073,18075,18077,18079,18081,18083,18086,18088,18090,18092],{"class":5386,"line":6737},[5384,18074,5523],{"class":5390},[5384,18076,5526],{"class":5509},[5384,18078,5529],{"class":5428},[5384,18080,16872],{"class":5394},[5384,18082,5535],{"class":5428},[5384,18084,18085],{"class":5509},"y_train.grad_fn",[5384,18087,5545],{"class":5428},[5384,18089,6238],{"class":6237},[5384,18091,5548],{"class":5394},[5384,18093,5551],{"class":5509},[5384,18095,18096],{"class":5386,"line":6760},[5384,18097,5517],{"emptyLinePlaceholder":5516},[5384,18099,18100],{"class":5386,"line":7162},[5384,18101,18102],{"class":6008},"# Без градієнтів (для inference)\n",[5384,18104,18105,18108],{"class":5386,"line":7172},[5384,18106,18107],{"class":5505},"with",[5384,18109,18110],{"class":5509}," torch.no_grad():\n",[5384,18112,18113],{"class":5386,"line":9675},[5384,18114,18115],{"class":5509},"    y_inference = w * x\n",[5384,18117,18118,18120,18122,18124,18127,18129,18132,18134,18136],{"class":5386,"line":9681},[5384,18119,5589],{"class":5390},[5384,18121,5526],{"class":5509},[5384,18123,5529],{"class":5428},[5384,18125,18126],{"class":5394},"\"Інференс: y = ",[5384,18128,5535],{"class":5428},[5384,18130,18131],{"class":5509},"y_inference",[5384,18133,5545],{"class":5428},[5384,18135,5548],{"class":5394},[5384,18137,5551],{"class":5509},[5384,18139,18140,18142,18144,18146,18148,18150,18153,18155,18157],{"class":5386,"line":9687},[5384,18141,5589],{"class":5390},[5384,18143,5526],{"class":5509},[5384,18145,5529],{"class":5428},[5384,18147,18059],{"class":5394},[5384,18149,5535],{"class":5428},[5384,18151,18152],{"class":5509},"y_inference.requires_grad",[5384,18154,5545],{"class":5428},[5384,18156,5548],{"class":5394},[5384,18158,5551],{"class":5509},[5384,18160,18161,18163,18165,18167,18169,18171,18174,18176,18178],{"class":5386,"line":9710},[5384,18162,5589],{"class":5390},[5384,18164,5526],{"class":5509},[5384,18166,5529],{"class":5428},[5384,18168,16872],{"class":5394},[5384,18170,5535],{"class":5428},[5384,18172,18173],{"class":5509},"y_inference.grad_fn",[5384,18175,5545],{"class":5428},[5384,18177,5548],{"class":5394},[5384,18179,5551],{"class":5509},[5615,18181,18182],{"v-slot:output":5379},[5618,18183,18184,18205],{},[5621,18185,18186],{},[5624,18187,18188,18191,18194,18198,18202],{},[5627,18189,18190],{"align":5629},"Режим виконання",[5627,18192,18193],{"align":5636},"Значення $y$",[5627,18195,18196],{"align":5636},[5381,18197,9535],{},[5627,18199,18200],{"align":5636},[5381,18201,15996],{},[5627,18203,18204],{"align":5629},"Використання пам'яті",[5639,18206,18207,18229],{},[5624,18208,18209,18214,18218,18222,18226],{},[5644,18210,18211],{"align":5629},[5220,18212,18213],{},"Тренування (стандартно)",[5644,18215,18216],{"align":5636},[5381,18217,17757],{},[5644,18219,18220],{"align":5636},[5381,18221,5668],{},[5644,18223,18224],{"align":5636},[5381,18225,16621],{},[5644,18227,18228],{"align":5629},"Зберігає граф обчислень",[5624,18230,18231,18239,18243,18247,18252],{},[5644,18232,18233],{"align":5629},[5220,18234,18235,18236],{},"З контекстом ",[5381,18237,18238],{},"torch.no_grad()",[5644,18240,18241],{"align":5636},[5381,18242,17757],{},[5644,18244,18245],{"align":5636},[5381,18246,9818],{},[5644,18248,18249],{"align":5636},[5381,18250,18251],{},"None",[5644,18253,18254],{"align":5629},"⚡ Економія пам'яті та прискорення",[6588,18256,18257,18264,18281],{},[5262,18258,18259],{},[5220,18260,18261,18262,9443],{},"Коли використовувати ",[5381,18263,18238],{},[5194,18265,18266,18269,18275,18278],{},[5197,18267,18268],{},"Валідація моделі на тестових даних",[5197,18270,18271,18272,5227],{},"Генерація передбачень (",[5224,18273,18274],{},"inference",[5197,18276,18277],{},"Обчислення метрик (accuracy, F1-score)",[5197,18279,18280],{},"Будь-які операції, де не потрібні градієнти",[5262,18282,18283,18286],{},[5220,18284,18285],{},"Переваги:"," Економія пам'яті та прискорення обчислень (не потрібно будувати граф).",[5979,18288,18290,18293],{"id":18289},"detach-відокремлення-тензора-від-графа",[5381,18291,18292],{},".detach()"," — відокремлення тензора від графа",[5262,18295,16976,18296,18298,18299,18302],{},[5381,18297,18292],{}," створює тензор, що ділить пам'ять з оригіналом, але ",[5220,18300,18301],{},"не бере участь"," у графі обчислень:",[5490,18304,18306],{"title":18305},"detach_method.ipynb",[5987,18307,18308,18531],{"execution-count":5493},[5374,18309,18311],{"className":5496,"code":18310,"language":5498,"meta":5379,"style":5379},"import torch\n\nx = torch.tensor(5.0, requires_grad=True)\ny = x ** 2\n\nprint(f\"y = {y}\")\nprint(f\"y.requires_grad: {y.requires_grad}\\n\")\n\n# Відокремлюємо від графа\ny_detached = y.detach()\nprint(f\"y_detached = {y_detached}\")\nprint(f\"y_detached.requires_grad: {y_detached.requires_grad}\")\nprint(f\"y_detached.grad_fn: {y_detached.grad_fn}\\n\")\n\n# Можна використовувати у подальших обчисленнях без gradients\nz = y_detached * 2\nprint(f\"z = y_detached * 2 = {z}\")\nprint(f\"z.requires_grad: {z.requires_grad}\")\n",[5381,18312,18313,18319,18323,18339,18345,18349,18369,18392,18396,18401,18406,18428,18450,18474,18478,18483,18490,18511],{"__ignoreMap":5379},[5384,18314,18315,18317],{"class":5386,"line":5387},[5384,18316,5506],{"class":5505},[5384,18318,5510],{"class":5509},[5384,18320,18321],{"class":5386,"line":5513},[5384,18322,5517],{"emptyLinePlaceholder":5516},[5384,18324,18325,18327,18329,18331,18333,18335,18337],{"class":5386,"line":5520},[5384,18326,15825],{"class":5509},[5384,18328,8450],{"class":5604},[5384,18330,6019],{"class":5509},[5384,18332,9535],{"class":5541},[5384,18334,7635],{"class":5509},[5384,18336,5668],{"class":5428},[5384,18338,5551],{"class":5509},[5384,18340,18341,18343],{"class":5386,"line":5554},[5384,18342,17650],{"class":5509},[5384,18344,15489],{"class":5604},[5384,18346,18347],{"class":5386,"line":5577},[5384,18348,5517],{"emptyLinePlaceholder":5516},[5384,18350,18351,18353,18355,18357,18359,18361,18363,18365,18367],{"class":5386,"line":5586},[5384,18352,5523],{"class":5390},[5384,18354,5526],{"class":5509},[5384,18356,5529],{"class":5428},[5384,18358,16829],{"class":5394},[5384,18360,5535],{"class":5428},[5384,18362,15466],{"class":5509},[5384,18364,5545],{"class":5428},[5384,18366,5548],{"class":5394},[5384,18368,5551],{"class":5509},[5384,18370,18371,18373,18375,18377,18379,18381,18384,18386,18388,18390],{"class":5386,"line":6086},[5384,18372,5523],{"class":5390},[5384,18374,5526],{"class":5509},[5384,18376,5529],{"class":5428},[5384,18378,18059],{"class":5394},[5384,18380,5535],{"class":5428},[5384,18382,18383],{"class":5509},"y.requires_grad",[5384,18385,5545],{"class":5428},[5384,18387,6238],{"class":6237},[5384,18389,5548],{"class":5394},[5384,18391,5551],{"class":5509},[5384,18393,18394],{"class":5386,"line":6273},[5384,18395,5517],{"emptyLinePlaceholder":5516},[5384,18397,18398],{"class":5386,"line":6713},[5384,18399,18400],{"class":6008},"# Відокремлюємо від графа\n",[5384,18402,18403],{"class":5386,"line":6737},[5384,18404,18405],{"class":5509},"y_detached = y.detach()\n",[5384,18407,18408,18410,18412,18414,18417,18419,18422,18424,18426],{"class":5386,"line":6760},[5384,18409,5523],{"class":5390},[5384,18411,5526],{"class":5509},[5384,18413,5529],{"class":5428},[5384,18415,18416],{"class":5394},"\"y_detached = ",[5384,18418,5535],{"class":5428},[5384,18420,18421],{"class":5509},"y_detached",[5384,18423,5545],{"class":5428},[5384,18425,5548],{"class":5394},[5384,18427,5551],{"class":5509},[5384,18429,18430,18432,18434,18436,18439,18441,18444,18446,18448],{"class":5386,"line":7162},[5384,18431,5523],{"class":5390},[5384,18433,5526],{"class":5509},[5384,18435,5529],{"class":5428},[5384,18437,18438],{"class":5394},"\"y_detached.requires_grad: ",[5384,18440,5535],{"class":5428},[5384,18442,18443],{"class":5509},"y_detached.requires_grad",[5384,18445,5545],{"class":5428},[5384,18447,5548],{"class":5394},[5384,18449,5551],{"class":5509},[5384,18451,18452,18454,18456,18458,18461,18463,18466,18468,18470,18472],{"class":5386,"line":7172},[5384,18453,5523],{"class":5390},[5384,18455,5526],{"class":5509},[5384,18457,5529],{"class":5428},[5384,18459,18460],{"class":5394},"\"y_detached.grad_fn: ",[5384,18462,5535],{"class":5428},[5384,18464,18465],{"class":5509},"y_detached.grad_fn",[5384,18467,5545],{"class":5428},[5384,18469,6238],{"class":6237},[5384,18471,5548],{"class":5394},[5384,18473,5551],{"class":5509},[5384,18475,18476],{"class":5386,"line":9675},[5384,18477,5517],{"emptyLinePlaceholder":5516},[5384,18479,18480],{"class":5386,"line":9681},[5384,18481,18482],{"class":6008},"# Можна використовувати у подальших обчисленнях без gradients\n",[5384,18484,18485,18488],{"class":5386,"line":9687},[5384,18486,18487],{"class":5509},"z = y_detached * ",[5384,18489,15489],{"class":5604},[5384,18491,18492,18494,18496,18498,18501,18503,18505,18507,18509],{"class":5386,"line":9710},[5384,18493,5523],{"class":5390},[5384,18495,5526],{"class":5509},[5384,18497,5529],{"class":5428},[5384,18499,18500],{"class":5394},"\"z = y_detached * 2 = ",[5384,18502,5535],{"class":5428},[5384,18504,15898],{"class":5509},[5384,18506,5545],{"class":5428},[5384,18508,5548],{"class":5394},[5384,18510,5551],{"class":5509},[5384,18512,18513,18515,18517,18519,18521,18523,18525,18527,18529],{"class":5386,"line":9732},[5384,18514,5523],{"class":5390},[5384,18516,5526],{"class":5509},[5384,18518,5529],{"class":5428},[5384,18520,16564],{"class":5394},[5384,18522,5535],{"class":5428},[5384,18524,16569],{"class":5509},[5384,18526,5545],{"class":5428},[5384,18528,5548],{"class":5394},[5384,18530,5551],{"class":5509},[5615,18532,18533,18534,18536,18537,18539,18540,18536,18543,18536,18546,18536,18548,18551,18552,18536,18557,18536,18559,18536,18561,18563],{"v-slot:output":5379},"| Менеджер контексту | Значення $y$ | ",[5381,18535,9535],{}," | ",[5381,18538,15996],{}," | Призначення |\n| :--- | :---: | :---: | :--- |\n| ",[5220,18541,18542],{},"Без контексту",[5381,18544,18545],{},"tensor(25.)",[5381,18547,5668],{},[5381,18549,18550],{},"\u003CPowBackward0>"," | Тренування моделей |\n| ",[5220,18553,18554],{},[5381,18555,18556],{},"torch.inference_mode()",[5381,18558,18545],{},[5381,18560,9818],{},[5381,18562,18251],{}," | 🚀 Оптимізований інференс у production |",[5479,18565,18566,18575],{},[5262,18567,18568],{},[5220,18569,9437,18570,9440,18573,9443],{},[5381,18571,18572],{},"no_grad()",[5381,18574,18292],{},[5618,18576,18577,18593],{},[5621,18578,18579],{},[5624,18580,18581,18584,18587,18590],{},[5627,18582,18583],{"align":5629},"Метод",[5627,18585,18586],{"align":5629},"Область дії",[5627,18588,18589],{"align":5629},"Створює копію?",[5627,18591,18592],{"align":5629},"Використання",[5639,18594,18595,18610,18625],{},[5624,18596,18597,18601,18604,18607],{},[5644,18598,18599],{"align":5629},[5381,18600,18238],{},[5644,18602,18603],{"align":5629},"Блок коду",[5644,18605,18606],{"align":5629},"Ні",[5644,18608,18609],{"align":5629},"Валідація, inference для всієї моделі",[5624,18611,18612,18616,18619,18622],{},[5644,18613,18614],{"align":5629},[5381,18615,18292],{},[5644,18617,18618],{"align":5629},"Конкретний тензор",[5644,18620,18621],{"align":5629},"Ні (ділить пам'ять)",[5644,18623,18624],{"align":5629},"Відокремлення проміжних результатів",[5624,18626,18627,18632,18634,18637],{},[5644,18628,18629],{"align":5629},[5381,18630,18631],{},".clone().detach()",[5644,18633,18618],{"align":5629},[5644,18635,18636],{"align":5629},"Так",[5644,18638,18639],{"align":5629},"Незалежна копія без градієнтів",[5979,18641,18643,18646],{"id":18642},"torchset_grad_enabledfalse-глобальне-вимикання",[5381,18644,18645],{},"torch.set_grad_enabled(False)"," — глобальне вимикання",[5262,18648,18649],{},"Рідко використовується, але корисний для умовного вимикання gradients:",[5490,18651,18653,18836],{"title":18652,"execution-count":5493},"set_grad_enabled.ipynb",[5374,18654,18656],{"className":5496,"code":18655,"language":5498,"meta":5379,"style":5379},"import torch\n\ndef compute(x, training=True):\n    torch.set_grad_enabled(training)\n    y = x ** 2\n    return y\n\nx = torch.tensor(3.0, requires_grad=True)\n\n# Режим тренування\nresult_train = compute(x, training=True)\nprint(f\"Тренування: {result_train}, requires_grad={result_train.requires_grad}\")\n\n# Режим інференсу\nresult_inference = compute(x, training=False)\nprint(f\"Інференс: {result_inference}, requires_grad={result_inference.requires_grad}\")\n",[5381,18657,18658,18664,18668,18690,18695,18702,18709,18713,18729,18733,18738,18751,18783,18787,18792,18805],{"__ignoreMap":5379},[5384,18659,18660,18662],{"class":5386,"line":5387},[5384,18661,5506],{"class":5505},[5384,18663,5510],{"class":5509},[5384,18665,18666],{"class":5386,"line":5513},[5384,18667,5517],{"emptyLinePlaceholder":5516},[5384,18669,18670,18672,18675,18677,18679,18681,18684,18686,18688],{"class":5386,"line":5520},[5384,18671,15453],{"class":5428},[5384,18673,18674],{"class":5390}," compute",[5384,18676,5526],{"class":5509},[5384,18678,15461],{"class":5541},[5384,18680,6019],{"class":5509},[5384,18682,18683],{"class":5541},"training",[5384,18685,7635],{"class":5509},[5384,18687,5668],{"class":5428},[5384,18689,15469],{"class":5509},[5384,18691,18692],{"class":5386,"line":5554},[5384,18693,18694],{"class":5509},"    torch.set_grad_enabled(training)\n",[5384,18696,18697,18700],{"class":5386,"line":5577},[5384,18698,18699],{"class":5509},"    y = x ** ",[5384,18701,15489],{"class":5604},[5384,18703,18704,18706],{"class":5386,"line":5586},[5384,18705,15474],{"class":5505},[5384,18707,18708],{"class":5509}," y\n",[5384,18710,18711],{"class":5386,"line":6086},[5384,18712,5517],{"emptyLinePlaceholder":5516},[5384,18714,18715,18717,18719,18721,18723,18725,18727],{"class":5386,"line":6273},[5384,18716,15825],{"class":5509},[5384,18718,8064],{"class":5604},[5384,18720,6019],{"class":5509},[5384,18722,9535],{"class":5541},[5384,18724,7635],{"class":5509},[5384,18726,5668],{"class":5428},[5384,18728,5551],{"class":5509},[5384,18730,18731],{"class":5386,"line":6713},[5384,18732,5517],{"emptyLinePlaceholder":5516},[5384,18734,18735],{"class":5386,"line":6737},[5384,18736,18737],{"class":6008},"# Режим тренування\n",[5384,18739,18740,18743,18745,18747,18749],{"class":5386,"line":6760},[5384,18741,18742],{"class":5509},"result_train = compute(x, ",[5384,18744,18683],{"class":5541},[5384,18746,7635],{"class":5509},[5384,18748,5668],{"class":5428},[5384,18750,5551],{"class":5509},[5384,18752,18753,18755,18757,18759,18762,18764,18767,18769,18772,18774,18777,18779,18781],{"class":5386,"line":7162},[5384,18754,5523],{"class":5390},[5384,18756,5526],{"class":5509},[5384,18758,5529],{"class":5428},[5384,18760,18761],{"class":5394},"\"Тренування: ",[5384,18763,5535],{"class":5428},[5384,18765,18766],{"class":5509},"result_train",[5384,18768,5545],{"class":5428},[5384,18770,18771],{"class":5394},", requires_grad=",[5384,18773,5535],{"class":5428},[5384,18775,18776],{"class":5509},"result_train.requires_grad",[5384,18778,5545],{"class":5428},[5384,18780,5548],{"class":5394},[5384,18782,5551],{"class":5509},[5384,18784,18785],{"class":5386,"line":7172},[5384,18786,5517],{"emptyLinePlaceholder":5516},[5384,18788,18789],{"class":5386,"line":9675},[5384,18790,18791],{"class":6008},"# Режим інференсу\n",[5384,18793,18794,18797,18799,18801,18803],{"class":5386,"line":9681},[5384,18795,18796],{"class":5509},"result_inference = compute(x, ",[5384,18798,18683],{"class":5541},[5384,18800,7635],{"class":5509},[5384,18802,9818],{"class":5428},[5384,18804,5551],{"class":5509},[5384,18806,18807,18809,18811,18813,18816,18818,18821,18823,18825,18827,18830,18832,18834],{"class":5386,"line":9687},[5384,18808,5523],{"class":5390},[5384,18810,5526],{"class":5509},[5384,18812,5529],{"class":5428},[5384,18814,18815],{"class":5394},"\"Інференс: ",[5384,18817,5535],{"class":5428},[5384,18819,18820],{"class":5509},"result_inference",[5384,18822,5545],{"class":5428},[5384,18824,18771],{"class":5394},[5384,18826,5535],{"class":5428},[5384,18828,18829],{"class":5509},"result_inference.requires_grad",[5384,18831,5545],{"class":5428},[5384,18833,5548],{"class":5394},[5384,18835,5551],{"class":5509},[5615,18837,18838],{"v-slot:output":5379},[5618,18839,18840,18859],{},[5621,18841,18842],{},[5624,18843,18844,18846,18848,18852,18856],{},[5627,18845,6117],{"align":5629},[5627,18847,5633],{"align":5636},[5627,18849,18850],{"align":5636},[5381,18851,9535],{},[5627,18853,18854],{"align":5636},[5381,18855,15996],{},[5627,18857,18858],{"align":5629},"Вплив на пам'ять",[5639,18860,18861,18884],{},[5624,18862,18863,18868,18873,18877,18881],{},[5644,18864,18865],{"align":5629},[5220,18866,18867],{},"Оригінал $y$",[5644,18869,18870],{"align":5636},[5381,18871,18872],{},"tensor(9.)",[5644,18874,18875],{"align":5636},[5381,18876,5668],{},[5644,18878,18879],{"align":5636},[5381,18880,18550],{},[5644,18882,18883],{"align":5629},"Зберігається в графі автограду",[5624,18885,18886,18891,18895,18899,18903],{},[5644,18887,18888],{"align":5629},[5220,18889,18890],{},"Відокремлений $y_{\\text{detached}}$",[5644,18892,18893],{"align":5636},[5381,18894,18872],{},[5644,18896,18897],{"align":5636},[5381,18898,9818],{},[5644,18900,18901],{"align":5636},[5381,18902,18251],{},[5644,18904,18905],{"align":5629},"⚡ Безпечний для виводу та NumPy",[5695,18907,18909],{"id":18908},"практичні-приклади-autograd","Практичні приклади autograd",[5262,18911,18912],{},"Розглянемо кілька реальних сценаріїв використання автоматичного диференціювання.",[5979,18914,18916],{"id":18915},"приклад-1-градієнти-для-простих-функцій","Приклад 1: Градієнти для простих функцій",[5262,18918,18919,18920,9443],{},"Обчислимо градієнти для функції ",[15379,18921],{"tex":18922,":inline":15382},"f(x) = x^3 - 4x^2 + 6x - 2",[5490,18924,18926],{"title":18925},"polynomial_gradients.ipynb",[5987,18927,18928,19192],{"execution-count":5493},[5374,18929,18931],{"className":5496,"code":18930,"language":5498,"meta":5379,"style":5379},"import torch\nimport matplotlib.pyplot as plt\n\n# Функція та її похідна\ndef f(x):\n    return x**3 - 4*x**2 + 6*x - 2\n\n# Обчислюємо градієнти для кількох точок\nx_values = torch.linspace(-1, 4, 100, requires_grad=True)\ny_values = f(x_values)\n\n# Обчислюємо градієнт у кожній точці\ngradients = []\nfor i in range(len(x_values)):\n    if x_values.grad is not None:\n        x_values.grad.zero_()\n    \n    y = f(x_values[i])\n    y.backward()\n    gradients.append(x_values.grad[i].item())\n\nprint(f\"Градієнт у точці x=0: {gradients[50]:.4f}\")\nprint(f\"Градієнт у точці x=1: {gradients[60]:.4f}\")\nprint(f\"Градієнт у точці x=2: {gradients[70]:.4f}\")\n",[5381,18932,18933,18939,18951,18955,18960,18972,18998,19002,19007,19032,19037,19041,19046,19051,19068,19087,19092,19096,19101,19106,19111,19115,19141,19166],{"__ignoreMap":5379},[5384,18934,18935,18937],{"class":5386,"line":5387},[5384,18936,5506],{"class":5505},[5384,18938,5510],{"class":5509},[5384,18940,18941,18943,18946,18948],{"class":5386,"line":5513},[5384,18942,5506],{"class":5505},[5384,18944,18945],{"class":5509}," matplotlib.pyplot ",[5384,18947,6629],{"class":5505},[5384,18949,18950],{"class":5509}," plt\n",[5384,18952,18953],{"class":5386,"line":5520},[5384,18954,5517],{"emptyLinePlaceholder":5516},[5384,18956,18957],{"class":5386,"line":5554},[5384,18958,18959],{"class":6008},"# Функція та її похідна\n",[5384,18961,18962,18964,18966,18968,18970],{"class":5386,"line":5577},[5384,18963,15453],{"class":5428},[5384,18965,15456],{"class":5390},[5384,18967,5526],{"class":5509},[5384,18969,15461],{"class":5541},[5384,18971,15469],{"class":5509},[5384,18973,18974,18976,18978,18980,18982,18984,18987,18989,18991,18993,18996],{"class":5386,"line":5586},[5384,18975,15474],{"class":5505},[5384,18977,15477],{"class":5509},[5384,18979,5743],{"class":5604},[5384,18981,14056],{"class":5509},[5384,18983,6031],{"class":5604},[5384,18985,18986],{"class":5509},"*x**",[5384,18988,6022],{"class":5604},[5384,18990,12085],{"class":5509},[5384,18992,6216],{"class":5604},[5384,18994,18995],{"class":5509},"*x - ",[5384,18997,15489],{"class":5604},[5384,18999,19000],{"class":5386,"line":6086},[5384,19001,5517],{"emptyLinePlaceholder":5516},[5384,19003,19004],{"class":5386,"line":6273},[5384,19005,19006],{"class":6008},"# Обчислюємо градієнти для кількох точок\n",[5384,19008,19009,19012,19014,19016,19018,19020,19022,19024,19026,19028,19030],{"class":5386,"line":6713},[5384,19010,19011],{"class":5509},"x_values = torch.linspace(-",[5384,19013,5493],{"class":5604},[5384,19015,6019],{"class":5509},[5384,19017,6031],{"class":5604},[5384,19019,6019],{"class":5509},[5384,19021,13862],{"class":5604},[5384,19023,6019],{"class":5509},[5384,19025,9535],{"class":5541},[5384,19027,7635],{"class":5509},[5384,19029,5668],{"class":5428},[5384,19031,5551],{"class":5509},[5384,19033,19034],{"class":5386,"line":6737},[5384,19035,19036],{"class":5509},"y_values = f(x_values)\n",[5384,19038,19039],{"class":5386,"line":6760},[5384,19040,5517],{"emptyLinePlaceholder":5516},[5384,19042,19043],{"class":5386,"line":7162},[5384,19044,19045],{"class":6008},"# Обчислюємо градієнт у кожній точці\n",[5384,19047,19048],{"class":5386,"line":7172},[5384,19049,19050],{"class":5509},"gradients = []\n",[5384,19052,19053,19055,19057,19059,19061,19063,19065],{"class":5386,"line":9675},[5384,19054,14794],{"class":5505},[5384,19056,14797],{"class":5509},[5384,19058,14800],{"class":5505},[5384,19060,14803],{"class":5390},[5384,19062,5526],{"class":5509},[5384,19064,14782],{"class":5390},[5384,19066,19067],{"class":5509},"(x_values)):\n",[5384,19069,19070,19073,19076,19079,19082,19085],{"class":5386,"line":9681},[5384,19071,19072],{"class":5505},"    if",[5384,19074,19075],{"class":5509}," x_values.grad ",[5384,19077,19078],{"class":5428},"is",[5384,19080,19081],{"class":5428}," not",[5384,19083,19084],{"class":5428}," None",[5384,19086,8499],{"class":5509},[5384,19088,19089],{"class":5386,"line":9687},[5384,19090,19091],{"class":5509},"        x_values.grad.zero_()\n",[5384,19093,19094],{"class":5386,"line":9710},[5384,19095,8432],{"class":5509},[5384,19097,19098],{"class":5386,"line":9732},[5384,19099,19100],{"class":5509},"    y = f(x_values[i])\n",[5384,19102,19103],{"class":5386,"line":10489},[5384,19104,19105],{"class":5509},"    y.backward()\n",[5384,19107,19108],{"class":5386,"line":10494},[5384,19109,19110],{"class":5509},"    gradients.append(x_values.grad[i].item())\n",[5384,19112,19113],{"class":5386,"line":10500},[5384,19114,5517],{"emptyLinePlaceholder":5516},[5384,19116,19117,19119,19121,19123,19126,19128,19131,19133,19135,19137,19139],{"class":5386,"line":12926},[5384,19118,5523],{"class":5390},[5384,19120,5526],{"class":5509},[5384,19122,5529],{"class":5428},[5384,19124,19125],{"class":5394},"\"Градієнт у точці x=0: ",[5384,19127,5535],{"class":5428},[5384,19129,19130],{"class":5509},"gradients[",[5384,19132,13857],{"class":5604},[5384,19134,10402],{"class":5509},[5384,19136,14103],{"class":5428},[5384,19138,5548],{"class":5394},[5384,19140,5551],{"class":5509},[5384,19142,19143,19145,19147,19149,19152,19154,19156,19158,19160,19162,19164],{"class":5386,"line":12953},[5384,19144,5523],{"class":5390},[5384,19146,5526],{"class":5509},[5384,19148,5529],{"class":5428},[5384,19150,19151],{"class":5394},"\"Градієнт у точці x=1: ",[5384,19153,5535],{"class":5428},[5384,19155,19130],{"class":5509},[5384,19157,8936],{"class":5604},[5384,19159,10402],{"class":5509},[5384,19161,14103],{"class":5428},[5384,19163,5548],{"class":5394},[5384,19165,5551],{"class":5509},[5384,19167,19168,19170,19172,19174,19177,19179,19181,19184,19186,19188,19190],{"class":5386,"line":16156},[5384,19169,5523],{"class":5390},[5384,19171,5526],{"class":5509},[5384,19173,5529],{"class":5428},[5384,19175,19176],{"class":5394},"\"Градієнт у точці x=2: ",[5384,19178,5535],{"class":5428},[5384,19180,19130],{"class":5509},[5384,19182,19183],{"class":5604},"70",[5384,19185,10402],{"class":5509},[5384,19187,14103],{"class":5428},[5384,19189,5548],{"class":5394},[5384,19191,5551],{"class":5509},[5615,19193,19194],{"v-slot:output":5379},[5618,19195,19196,19211],{},[5621,19197,19198],{},[5624,19199,19200,19203,19206,19208],{},[5627,19201,19202],{"align":5636},"Точка обчислення ($x$)",[5627,19204,19205],{"align":5636},"Аналітична похідна $f'(x) = 3x^2 - 8x + 6$",[5627,19207,15682],{"align":5636},[5627,19209,19210],{"align":5636},"Статус верифікації",[5639,19212,19213,19229,19244],{},[5624,19214,19215,19218,19221,19226],{},[5644,19216,19217],{"align":5636},"$x = 0.0$",[5644,19219,19220],{"align":5636},"$3(0)^2 - 8(0) + 6 = \\mathbf{6.0000}$",[5644,19222,19223],{"align":5636},[5381,19224,19225],{},"6.0000",[5644,19227,19228],{"align":5636},"✅ Точний збіг",[5624,19230,19231,19234,19237,19242],{},[5644,19232,19233],{"align":5636},"$x = 1.0$",[5644,19235,19236],{"align":5636},"$3(1)^2 - 8(1) + 6 = \\mathbf{1.0000}$",[5644,19238,19239],{"align":5636},[5381,19240,19241],{},"1.0000",[5644,19243,19228],{"align":5636},[5624,19245,19246,19249,19252,19256],{},[5644,19247,19248],{"align":5636},"$x = 2.0$",[5644,19250,19251],{"align":5636},"$3(2)^2 - 8(2) + 6 = \\mathbf{2.0000}$",[5644,19253,19254],{"align":5636},[5381,19255,17518],{},[5644,19257,19228],{"align":5636},[5979,19259,19261],{"id":19260},"приклад-2-градієнтний-спуск-вручну","Приклад 2: Градієнтний спуск вручну",[5262,19263,19264,19265,9443],{},"Реалізуємо простий градієнтний спуск для мінімізації функції ",[15379,19266],{"tex":19267,":inline":15382},"f(x) = (x - 3)^2",[5490,19269,19271],{"title":19270},"gradient_descent_manual.ipynb",[5987,19272,19273,19549],{"execution-count":5493},[5374,19274,19276],{"className":5496,"code":19275,"language":5498,"meta":5379,"style":5379},"import torch\n\n# Початкове значення (далеко від мінімуму)\nx = torch.tensor(-2.0, requires_grad=True)\nlearning_rate = 0.1\nnum_iterations = 50\n\nprint(f\"Початок: x = {x.item():.4f}, f(x) = {((x - 3)**2).item():.4f}\\n\")\n\nfor i in range(num_iterations):\n    # Forward pass\n    loss = (x - 3) ** 2\n    \n    # Backward pass\n    loss.backward()\n    \n    # Оновлення параметра (gradient descent step)\n    with torch.no_grad():\n        x -= learning_rate * x.grad\n    \n    # Обнулення градієнтів\n    x.grad.zero_()\n    \n    if i % 10 == 0:\n        print(f\"Ітерація {i:2d}: x = {x.item():.4f}, loss = {loss.item():.4f}\")\n\nprint(f\"\\nКінець: x = {x.item():.4f} (оптимум: x=3)\")\n",[5381,19277,19278,19284,19288,19293,19310,19318,19326,19330,19374,19378,19391,19396,19408,19412,19417,19422,19426,19431,19438,19443,19447,19452,19457,19461,19477,19519,19523],{"__ignoreMap":5379},[5384,19279,19280,19282],{"class":5386,"line":5387},[5384,19281,5506],{"class":5505},[5384,19283,5510],{"class":5509},[5384,19285,19286],{"class":5386,"line":5513},[5384,19287,5517],{"emptyLinePlaceholder":5516},[5384,19289,19290],{"class":5386,"line":5520},[5384,19291,19292],{"class":6008},"# Початкове значення (далеко від мінімуму)\n",[5384,19294,19295,19298,19300,19302,19304,19306,19308],{"class":5386,"line":5554},[5384,19296,19297],{"class":5509},"x = torch.tensor(-",[5384,19299,8059],{"class":5604},[5384,19301,6019],{"class":5509},[5384,19303,9535],{"class":5541},[5384,19305,7635],{"class":5509},[5384,19307,5668],{"class":5428},[5384,19309,5551],{"class":5509},[5384,19311,19312,19315],{"class":5386,"line":5577},[5384,19313,19314],{"class":5509},"learning_rate = ",[5384,19316,19317],{"class":5604},"0.1\n",[5384,19319,19320,19323],{"class":5386,"line":5586},[5384,19321,19322],{"class":5509},"num_iterations = ",[5384,19324,19325],{"class":5604},"50\n",[5384,19327,19328],{"class":5386,"line":6086},[5384,19329,5517],{"emptyLinePlaceholder":5516},[5384,19331,19332,19334,19336,19338,19341,19343,19346,19348,19351,19353,19356,19358,19361,19363,19366,19368,19370,19372],{"class":5386,"line":6273},[5384,19333,5523],{"class":5390},[5384,19335,5526],{"class":5509},[5384,19337,5529],{"class":5428},[5384,19339,19340],{"class":5394},"\"Початок: x = ",[5384,19342,5535],{"class":5428},[5384,19344,19345],{"class":5509},"x.item()",[5384,19347,14103],{"class":5428},[5384,19349,19350],{"class":5394},", f(x) = ",[5384,19352,5535],{"class":5428},[5384,19354,19355],{"class":5509},"((x - ",[5384,19357,5743],{"class":5604},[5384,19359,19360],{"class":5509},")**",[5384,19362,6022],{"class":5604},[5384,19364,19365],{"class":5509},").item()",[5384,19367,14103],{"class":5428},[5384,19369,6238],{"class":6237},[5384,19371,5548],{"class":5394},[5384,19373,5551],{"class":5509},[5384,19375,19376],{"class":5386,"line":6713},[5384,19377,5517],{"emptyLinePlaceholder":5516},[5384,19379,19380,19382,19384,19386,19388],{"class":5386,"line":6737},[5384,19381,14794],{"class":5505},[5384,19383,14797],{"class":5509},[5384,19385,14800],{"class":5505},[5384,19387,14803],{"class":5390},[5384,19389,19390],{"class":5509},"(num_iterations):\n",[5384,19392,19393],{"class":5386,"line":6760},[5384,19394,19395],{"class":6008},"    # Forward pass\n",[5384,19397,19398,19401,19403,19406],{"class":5386,"line":7162},[5384,19399,19400],{"class":5509},"    loss = (x - ",[5384,19402,5743],{"class":5604},[5384,19404,19405],{"class":5509},") ** ",[5384,19407,15489],{"class":5604},[5384,19409,19410],{"class":5386,"line":7172},[5384,19411,8432],{"class":5509},[5384,19413,19414],{"class":5386,"line":9675},[5384,19415,19416],{"class":6008},"    # Backward pass\n",[5384,19418,19419],{"class":5386,"line":9681},[5384,19420,19421],{"class":5509},"    loss.backward()\n",[5384,19423,19424],{"class":5386,"line":9687},[5384,19425,8432],{"class":5509},[5384,19427,19428],{"class":5386,"line":9710},[5384,19429,19430],{"class":6008},"    # Оновлення параметра (gradient descent step)\n",[5384,19432,19433,19436],{"class":5386,"line":9732},[5384,19434,19435],{"class":5505},"    with",[5384,19437,18110],{"class":5509},[5384,19439,19440],{"class":5386,"line":10489},[5384,19441,19442],{"class":5509},"        x -= learning_rate * x.grad\n",[5384,19444,19445],{"class":5386,"line":10494},[5384,19446,8432],{"class":5509},[5384,19448,19449],{"class":5386,"line":10500},[5384,19450,19451],{"class":6008},"    # Обнулення градієнтів\n",[5384,19453,19454],{"class":5386,"line":12926},[5384,19455,19456],{"class":5509},"    x.grad.zero_()\n",[5384,19458,19459],{"class":5386,"line":12953},[5384,19460,8432],{"class":5509},[5384,19462,19463,19465,19468,19470,19473,19475],{"class":5386,"line":16156},[5384,19464,19072],{"class":5505},[5384,19466,19467],{"class":5509}," i % ",[5384,19469,7325],{"class":5604},[5384,19471,19472],{"class":5509}," == ",[5384,19474,5605],{"class":5604},[5384,19476,8499],{"class":5509},[5384,19478,19479,19482,19484,19486,19489,19491,19494,19497,19500,19502,19504,19506,19509,19511,19513,19515,19517],{"class":5386,"line":16162},[5384,19480,19481],{"class":5390},"        print",[5384,19483,5526],{"class":5509},[5384,19485,5529],{"class":5428},[5384,19487,19488],{"class":5394},"\"Ітерація ",[5384,19490,5535],{"class":5428},[5384,19492,19493],{"class":5509},"i",[5384,19495,19496],{"class":5428},":2d}",[5384,19498,19499],{"class":5394},": x = ",[5384,19501,5535],{"class":5428},[5384,19503,19345],{"class":5509},[5384,19505,14103],{"class":5428},[5384,19507,19508],{"class":5394},", loss = ",[5384,19510,5535],{"class":5428},[5384,19512,17404],{"class":5509},[5384,19514,14103],{"class":5428},[5384,19516,5548],{"class":5394},[5384,19518,5551],{"class":5509},[5384,19520,19521],{"class":5386,"line":16168},[5384,19522,5517],{"emptyLinePlaceholder":5516},[5384,19524,19525,19527,19529,19531,19533,19535,19538,19540,19542,19544,19547],{"class":5386,"line":16173},[5384,19526,5523],{"class":5390},[5384,19528,5526],{"class":5509},[5384,19530,5529],{"class":5428},[5384,19532,5548],{"class":5394},[5384,19534,6238],{"class":6237},[5384,19536,19537],{"class":5394},"Кінець: x = ",[5384,19539,5535],{"class":5428},[5384,19541,19345],{"class":5509},[5384,19543,14103],{"class":5428},[5384,19545,19546],{"class":5394}," (оптимум: x=3)\"",[5384,19548,5551],{"class":5509},[5615,19550,19551],{"v-slot:output":5379},[5618,19552,19553,19572],{},[5621,19554,19555],{},[5624,19556,19557,19560,19563,19566,19569],{},[5627,19558,19559],{"align":5636},"Ітерація",[5627,19561,19562],{"align":5636},"Поточне значення $x$",[5627,19564,19565],{"align":5636},"Значення функції втрат $Loss = (x-3)^2$",[5627,19567,19568],{"align":5636},"Градієнт $\\nabla Loss = 2(x-3)$",[5627,19570,19571],{"align":5636},"Крок оновлення ($lr=0.1$)",[5639,19573,19574,19599,19623,19647,19671,19695,19720],{},[5624,19575,19576,19581,19586,19591,19596],{},[5644,19577,19578],{"align":5636},[5220,19579,19580],{},"Початок",[5644,19582,19583],{"align":5636},[5381,19584,19585],{},"-2.0000",[5644,19587,19588],{"align":5636},[5381,19589,19590],{},"25.0000",[5644,19592,19593],{"align":5636},[5381,19594,19595],{},"-10.0000",[5644,19597,19598],{"align":5636},"Старт з віддаленої точки",[5624,19600,19601,19605,19610,19615,19620],{},[5644,19602,19603],{"align":5636},[5220,19604,5605],{},[5644,19606,19607],{"align":5636},[5381,19608,19609],{},"-1.0000",[5644,19611,19612],{"align":5636},[5381,19613,19614],{},"16.0000",[5644,19616,19617],{"align":5636},[5381,19618,19619],{},"-8.0000",[5644,19621,19622],{"align":5636},"$-2.0 - 0.1 \\times (-10) = -1.0$",[5624,19624,19625,19629,19634,19639,19644],{},[5644,19626,19627],{"align":5636},[5220,19628,7325],{},[5644,19630,19631],{"align":5636},[5381,19632,19633],{},"+1.4305",[5644,19635,19636],{"align":5636},[5381,19637,19638],{},"2.4641",[5644,19640,19641],{"align":5636},[5381,19642,19643],{},"-3.1390",[5644,19645,19646],{"align":5636},"Швидке наближення",[5624,19648,19649,19653,19658,19663,19668],{},[5644,19650,19651],{"align":5636},[5220,19652,11555],{},[5644,19654,19655],{"align":5636},[5381,19656,19657],{},"+2.3906",[5644,19659,19660],{"align":5636},[5381,19661,19662],{},"0.3713",[5644,19664,19665],{"align":5636},[5381,19666,19667],{},"-1.2188",[5644,19669,19670],{"align":5636},"Дрібні кроки біля мінімуму",[5624,19672,19673,19677,19682,19687,19692],{},[5644,19674,19675],{"align":5636},[5220,19676,11022],{},[5644,19678,19679],{"align":5636},[5381,19680,19681],{},"+2.7619",[5644,19683,19684],{"align":5636},[5381,19685,19686],{},"0.0567",[5644,19688,19689],{"align":5636},[5381,19690,19691],{},"-0.4762",[5644,19693,19694],{"align":5636},"Висока точність",[5624,19696,19697,19702,19707,19712,19717],{},[5644,19698,19699],{"align":5636},[5220,19700,19701],{},"40",[5644,19703,19704],{"align":5636},[5381,19705,19706],{},"+2.9058",[5644,19708,19709],{"align":5636},[5381,19710,19711],{},"0.0089",[5644,19713,19714],{"align":5636},[5381,19715,19716],{},"-0.1884",[5644,19718,19719],{"align":5636},"Практична збіжність",[5624,19721,19722,19727,19732,19737,19742],{},[5644,19723,19724],{"align":5636},[5220,19725,19726],{},"Фінал (50)",[5644,19728,19729],{"align":5636},[5381,19730,19731],{},"+2.9526",[5644,19733,19734],{"align":5636},[5381,19735,19736],{},"0.0022",[5644,19738,19739],{"align":5636},[5381,19740,19741],{},"-0.0948",[5644,19743,19744],{"align":5636},"🎯 Збіжність до оптимуму $x^* = 3.0$",[5351,19746,19747],{},[5262,19748,19749,19752,19753,19756],{},[5220,19750,19751],{},"Що відбувається?"," На кожній ітерації ми обчислюємо градієнт функції втрат, який показує напрямок найбільшого зростання. Рухаючись у ",[5220,19754,19755],{},"протилежному напрямку"," (мінус градієнт), ми поступово наближаємося до мінімуму.",[5262,19758,19759],{},[5268,19760],{"alt":19761,"className":19762,"src":19763},"Оптимізація функції та градієнтний спуск на 3D чаші поверхні втрат за допомогою Autograd",[5272],"\u002Fimages\u002Fai-python\u002Fpytorch-intro\u002Ftensors-and-autograd\u002F08.png",[5275,19765,19767,19769],{"alt":19766},"Алгоритм градієнтного спуску за допомогою Autograd",[5262,19768,5280],{},[5262,19770,19771],{},"start\n:Ініціалізація параметрів x (requires_grad=True);\nrepeat\n:Прямий прохід: обчислення f(x) = (x - 3)²;\n:Зворотний прохід: loss.backward();\n:Отримання градієнта ∇f = x.grad;\npartition \"torch.no_grad()\" {\n:Оновлення параметра: x := x - α * ∇f;\n:Очищення градієнта: x.grad.zero_();\n}\nrepeat while (критерій зупинки \u002F епохи досягнуто?) is (ні)\n->так;\n:Оптимальне значення параметра x*;\nstop\n@enduml",[5979,19773,19775],{"id":19774},"приклад-3-навчання-лінійної-регресії","Приклад 3: Навчання лінійної регресії",[5262,19777,19778],{},"Повний приклад навчання простої моделі на синтетичних даних:",[5490,19780,19782],{"title":19781},"linear_regression_autograd.ipynb",[5987,19783,19784,20209],{"execution-count":5493},[5374,19785,19787],{"className":5496,"code":19786,"language":5498,"meta":5379,"style":5379},"import torch\nimport torch.nn.functional as F\n\n# Генеруємо синтетичні дані: y = 3x + 2 + шум\ntorch.manual_seed(42)\nX = torch.randn(100, 1)\ny_true = 3 * X + 2 + torch.randn(100, 1) * 0.3\n\n# Ініціалізація параметрів моделі\nw = torch.randn(1, 1, requires_grad=True)\nb = torch.randn(1, requires_grad=True)\n\nprint(f\"Початкові параметри:\")\nprint(f\"  w = {w.item():.4f}, b = {b.item():.4f}\\n\")\n\n# Навчання\nlearning_rate = 0.01\nepochs = 100\n\nfor epoch in range(epochs):\n    # Forward pass\n    y_pred = X @ w + b\n    loss = F.mse_loss(y_pred, y_true)\n    \n    # Backward pass\n    loss.backward()\n    \n    # Оновлення параметрів\n    with torch.no_grad():\n        w -= learning_rate * w.grad\n        b -= learning_rate * b.grad\n    \n    # Обнулення градієнтів\n    w.grad.zero_()\n    b.grad.zero_()\n    \n    if (epoch + 1) % 20 == 0:\n        print(f\"Епоха {epoch+1:3d}: loss = {loss.item():.4f}, w = {w.item():.4f}, b = {b.item():.4f}\")\n\nprint(f\"\\nІстинні параметри: w = 3.0, b = 2.0\")\nprint(f\"Навчені параметри: w = {w.item():.4f}, b = {b.item():.4f}\")\n",[5381,19788,19789,19795,19805,19809,19814,19822,19835,19861,19865,19870,19891,19908,19912,19925,19959,19963,19968,19975,19983,19987,20001,20005,20010,20015,20019,20023,20027,20031,20037,20044,20050,20056,20061,20066,20072,20078,20083,20104,20156,20161,20179],{"__ignoreMap":5379},[5384,19790,19791,19793],{"class":5386,"line":5387},[5384,19792,5506],{"class":5505},[5384,19794,5510],{"class":5509},[5384,19796,19797,19799,19801,19803],{"class":5386,"line":5513},[5384,19798,5506],{"class":5505},[5384,19800,14317],{"class":5509},[5384,19802,6629],{"class":5505},[5384,19804,14322],{"class":5509},[5384,19806,19807],{"class":5386,"line":5520},[5384,19808,5517],{"emptyLinePlaceholder":5516},[5384,19810,19811],{"class":5386,"line":5554},[5384,19812,19813],{"class":6008},"# Генеруємо синтетичні дані: y = 3x + 2 + шум\n",[5384,19815,19816,19818,19820],{"class":5386,"line":5577},[5384,19817,7909],{"class":5509},[5384,19819,7912],{"class":5604},[5384,19821,5551],{"class":5509},[5384,19823,19824,19827,19829,19831,19833],{"class":5386,"line":5586},[5384,19825,19826],{"class":5509},"X = torch.randn(",[5384,19828,13862],{"class":5604},[5384,19830,6019],{"class":5509},[5384,19832,5493],{"class":5604},[5384,19834,5551],{"class":5509},[5384,19836,19837,19840,19842,19845,19847,19850,19852,19854,19856,19858],{"class":5386,"line":6086},[5384,19838,19839],{"class":5509},"y_true = ",[5384,19841,5743],{"class":5604},[5384,19843,19844],{"class":5509}," * X + ",[5384,19846,6022],{"class":5604},[5384,19848,19849],{"class":5509}," + torch.randn(",[5384,19851,13862],{"class":5604},[5384,19853,6019],{"class":5509},[5384,19855,5493],{"class":5604},[5384,19857,14051],{"class":5509},[5384,19859,19860],{"class":5604},"0.3\n",[5384,19862,19863],{"class":5386,"line":6273},[5384,19864,5517],{"emptyLinePlaceholder":5516},[5384,19866,19867],{"class":5386,"line":6713},[5384,19868,19869],{"class":6008},"# Ініціалізація параметрів моделі\n",[5384,19871,19872,19875,19877,19879,19881,19883,19885,19887,19889],{"class":5386,"line":6737},[5384,19873,19874],{"class":5509},"w = torch.randn(",[5384,19876,5493],{"class":5604},[5384,19878,6019],{"class":5509},[5384,19880,5493],{"class":5604},[5384,19882,6019],{"class":5509},[5384,19884,9535],{"class":5541},[5384,19886,7635],{"class":5509},[5384,19888,5668],{"class":5428},[5384,19890,5551],{"class":5509},[5384,19892,19893,19896,19898,19900,19902,19904,19906],{"class":5386,"line":6760},[5384,19894,19895],{"class":5509},"b = torch.randn(",[5384,19897,5493],{"class":5604},[5384,19899,6019],{"class":5509},[5384,19901,9535],{"class":5541},[5384,19903,7635],{"class":5509},[5384,19905,5668],{"class":5428},[5384,19907,5551],{"class":5509},[5384,19909,19910],{"class":5386,"line":7162},[5384,19911,5517],{"emptyLinePlaceholder":5516},[5384,19913,19914,19916,19918,19920,19923],{"class":5386,"line":7172},[5384,19915,5523],{"class":5390},[5384,19917,5526],{"class":5509},[5384,19919,5529],{"class":5428},[5384,19921,19922],{"class":5394},"\"Початкові параметри:\"",[5384,19924,5551],{"class":5509},[5384,19926,19927,19929,19931,19933,19936,19938,19941,19943,19946,19948,19951,19953,19955,19957],{"class":5386,"line":9675},[5384,19928,5523],{"class":5390},[5384,19930,5526],{"class":5509},[5384,19932,5529],{"class":5428},[5384,19934,19935],{"class":5394},"\"  w = ",[5384,19937,5535],{"class":5428},[5384,19939,19940],{"class":5509},"w.item()",[5384,19942,14103],{"class":5428},[5384,19944,19945],{"class":5394},", b = ",[5384,19947,5535],{"class":5428},[5384,19949,19950],{"class":5509},"b.item()",[5384,19952,14103],{"class":5428},[5384,19954,6238],{"class":6237},[5384,19956,5548],{"class":5394},[5384,19958,5551],{"class":5509},[5384,19960,19961],{"class":5386,"line":9681},[5384,19962,5517],{"emptyLinePlaceholder":5516},[5384,19964,19965],{"class":5386,"line":9687},[5384,19966,19967],{"class":6008},"# Навчання\n",[5384,19969,19970,19972],{"class":5386,"line":9710},[5384,19971,19314],{"class":5509},[5384,19973,19974],{"class":5604},"0.01\n",[5384,19976,19977,19980],{"class":5386,"line":9732},[5384,19978,19979],{"class":5509},"epochs = ",[5384,19981,19982],{"class":5604},"100\n",[5384,19984,19985],{"class":5386,"line":10489},[5384,19986,5517],{"emptyLinePlaceholder":5516},[5384,19988,19989,19991,19994,19996,19998],{"class":5386,"line":10494},[5384,19990,14794],{"class":5505},[5384,19992,19993],{"class":5509}," epoch ",[5384,19995,14800],{"class":5505},[5384,19997,14803],{"class":5390},[5384,19999,20000],{"class":5509},"(epochs):\n",[5384,20002,20003],{"class":5386,"line":10500},[5384,20004,19395],{"class":6008},[5384,20006,20007],{"class":5386,"line":12926},[5384,20008,20009],{"class":5509},"    y_pred = X @ w + b\n",[5384,20011,20012],{"class":5386,"line":12953},[5384,20013,20014],{"class":5509},"    loss = F.mse_loss(y_pred, y_true)\n",[5384,20016,20017],{"class":5386,"line":16156},[5384,20018,8432],{"class":5509},[5384,20020,20021],{"class":5386,"line":16162},[5384,20022,19416],{"class":6008},[5384,20024,20025],{"class":5386,"line":16168},[5384,20026,19421],{"class":5509},[5384,20028,20029],{"class":5386,"line":16173},[5384,20030,8432],{"class":5509},[5384,20032,20034],{"class":5386,"line":20033},28,[5384,20035,20036],{"class":6008},"    # Оновлення параметрів\n",[5384,20038,20040,20042],{"class":5386,"line":20039},29,[5384,20041,19435],{"class":5505},[5384,20043,18110],{"class":5509},[5384,20045,20047],{"class":5386,"line":20046},30,[5384,20048,20049],{"class":5509},"        w -= learning_rate * w.grad\n",[5384,20051,20053],{"class":5386,"line":20052},31,[5384,20054,20055],{"class":5509},"        b -= learning_rate * b.grad\n",[5384,20057,20059],{"class":5386,"line":20058},32,[5384,20060,8432],{"class":5509},[5384,20062,20064],{"class":5386,"line":20063},33,[5384,20065,19451],{"class":6008},[5384,20067,20069],{"class":5386,"line":20068},34,[5384,20070,20071],{"class":5509},"    w.grad.zero_()\n",[5384,20073,20075],{"class":5386,"line":20074},35,[5384,20076,20077],{"class":5509},"    b.grad.zero_()\n",[5384,20079,20081],{"class":5386,"line":20080},36,[5384,20082,8432],{"class":5509},[5384,20084,20086,20088,20091,20093,20096,20098,20100,20102],{"class":5386,"line":20085},37,[5384,20087,19072],{"class":5505},[5384,20089,20090],{"class":5509}," (epoch + ",[5384,20092,5493],{"class":5604},[5384,20094,20095],{"class":5509},") % ",[5384,20097,11555],{"class":5604},[5384,20099,19472],{"class":5509},[5384,20101,5605],{"class":5604},[5384,20103,8499],{"class":5509},[5384,20105,20107,20109,20111,20113,20116,20118,20121,20123,20126,20129,20131,20133,20135,20138,20140,20142,20144,20146,20148,20150,20152,20154],{"class":5386,"line":20106},38,[5384,20108,19481],{"class":5390},[5384,20110,5526],{"class":5509},[5384,20112,5529],{"class":5428},[5384,20114,20115],{"class":5394},"\"Епоха ",[5384,20117,5535],{"class":5428},[5384,20119,20120],{"class":5509},"epoch+",[5384,20122,5493],{"class":5604},[5384,20124,20125],{"class":5428},":3d}",[5384,20127,20128],{"class":5394},": loss = ",[5384,20130,5535],{"class":5428},[5384,20132,17404],{"class":5509},[5384,20134,14103],{"class":5428},[5384,20136,20137],{"class":5394},", w = ",[5384,20139,5535],{"class":5428},[5384,20141,19940],{"class":5509},[5384,20143,14103],{"class":5428},[5384,20145,19945],{"class":5394},[5384,20147,5535],{"class":5428},[5384,20149,19950],{"class":5509},[5384,20151,14103],{"class":5428},[5384,20153,5548],{"class":5394},[5384,20155,5551],{"class":5509},[5384,20157,20159],{"class":5386,"line":20158},39,[5384,20160,5517],{"emptyLinePlaceholder":5516},[5384,20162,20164,20166,20168,20170,20172,20174,20177],{"class":5386,"line":20163},40,[5384,20165,5523],{"class":5390},[5384,20167,5526],{"class":5509},[5384,20169,5529],{"class":5428},[5384,20171,5548],{"class":5394},[5384,20173,6238],{"class":6237},[5384,20175,20176],{"class":5394},"Істинні параметри: w = 3.0, b = 2.0\"",[5384,20178,5551],{"class":5509},[5384,20180,20182,20184,20186,20188,20191,20193,20195,20197,20199,20201,20203,20205,20207],{"class":5386,"line":20181},41,[5384,20183,5523],{"class":5390},[5384,20185,5526],{"class":5509},[5384,20187,5529],{"class":5428},[5384,20189,20190],{"class":5394},"\"Навчені параметри: w = ",[5384,20192,5535],{"class":5428},[5384,20194,19940],{"class":5509},[5384,20196,14103],{"class":5428},[5384,20198,19945],{"class":5394},[5384,20200,5535],{"class":5428},[5384,20202,19950],{"class":5509},[5384,20204,14103],{"class":5428},[5384,20206,5548],{"class":5394},[5384,20208,5551],{"class":5509},[5615,20210,20211],{"v-slot:output":5379},[5618,20212,20213,20236],{},[5621,20214,20215],{},[5624,20216,20217,20220,20223,20226,20229],{},[5627,20218,20219],{"align":5636},"Епоха навчання",[5627,20221,20222],{"align":5636},"Значення втрат (MSE Loss)",[5627,20224,20225],{"align":5636},"Параметр $w$ (вага)",[5627,20227,20228],{"align":5636},"Параметр $b$ (зміщення)",[5627,20230,20231,20232,20235],{"align":5629},"Відхилення від оптимуму ($w^",[5224,20233,20234],{},"=2.0, b^","=1.0$)",[5639,20237,20238,20260,20284,20308,20332,20355],{},[5624,20239,20240,20245,20247,20252,20257],{},[5644,20241,20242],{"align":5636},[5220,20243,20244],{},"Старт (0)",[5644,20246,10948],{"align":5636},[5644,20248,20249],{"align":5636},[5381,20250,20251],{},"0.3367",[5644,20253,20254],{"align":5636},[5381,20255,20256],{},"0.1288",[5644,20258,20259],{"align":5629},"Випадкова ініціалізація ваг",[5624,20261,20262,20266,20271,20276,20281],{},[5644,20263,20264],{"align":5636},[5220,20265,11555],{},[5644,20267,20268],{"align":5636},[5381,20269,20270],{},"0.043512",[5644,20272,20273],{"align":5636},[5381,20274,20275],{},"1.9542",[5644,20277,20278],{"align":5636},[5381,20279,20280],{},"1.0234",[5644,20282,20283],{"align":5629},"Швидке наближення до тренду",[5624,20285,20286,20290,20295,20300,20305],{},[5644,20287,20288],{"align":5636},[5220,20289,19701],{},[5644,20291,20292],{"align":5636},[5381,20293,20294],{},"0.000412",[5644,20296,20297],{"align":5636},[5381,20298,20299],{},"1.9954",[5644,20301,20302],{"align":5636},[5381,20303,20304],{},"1.0023",[5644,20306,20307],{"align":5629},"Точне налаштування параметрів",[5624,20309,20310,20314,20319,20324,20329],{},[5644,20311,20312],{"align":5636},[5220,20313,8936],{},[5644,20315,20316],{"align":5636},[5381,20317,20318],{},"0.000004",[5644,20320,20321],{"align":5636},[5381,20322,20323],{},"1.9995",[5644,20325,20326],{"align":5636},[5381,20327,20328],{},"1.0002",[5644,20330,20331],{"align":5629},"Практично точний збіг",[5624,20333,20334,20339,20344,20348,20352],{},[5644,20335,20336],{"align":5636},[5220,20337,20338],{},"80",[5644,20340,20341],{"align":5636},[5381,20342,20343],{},"0.000000",[5644,20345,20346],{"align":5636},[5381,20347,17518],{},[5644,20349,20350],{"align":5636},[5381,20351,19241],{},[5644,20353,20354],{"align":5629},"🎯 Повна збіжність",[5624,20356,20357,20362,20366,20370,20374],{},[5644,20358,20359],{"align":5636},[5220,20360,20361],{},"Фінал (100)",[5644,20363,20364],{"align":5636},[5381,20365,20343],{},[5644,20367,20368],{"align":5636},[5381,20369,17518],{},[5644,20371,20372],{"align":5636},[5381,20373,19241],{},[5644,20375,20376],{"align":5629},"🎯 Збіг: $w=2.0, b=1.0$",[5262,20378,20379],{},"Модель успішно знайшла параметри, дуже близькі до істинних! Це фундаментальний принцип навчання будь-яких нейронних мереж.",[5688,20381],{},[5257,20383,20385],{"id":20384},"резюме","Резюме",[5186,20387,20388,20427,20457,20485],{},[5189,20389,20391],{"icon":2865,"title":20390},"📊 Тензори",[5194,20392,20393,20396,20407,20418,20421],{},[5197,20394,20395],{},"Тензор — багатовимірний масив, основа PyTorch",[5197,20397,20398,20399,6019,20401,6019,20404],{},"Створення: ",[5381,20400,6593],{},[5381,20402,20403],{},"torch.zeros()",[5381,20405,20406],{},"torch.randn()",[5197,20408,20409,20410,6019,20412,6019,20414,6019,20416],{},"Атрибути: ",[5381,20411,6126],{},[5381,20413,6138],{},[5381,20415,8021],{},[5381,20417,9535],{},[5197,20419,20420],{},"Операції: індексація, broadcasting, математичні функції",[5197,20422,20423,20424],{},"GPU-прискорення через ",[5381,20425,20426],{},".to('cuda')",[5189,20428,20430],{"icon":4590,"title":20429},"🔄 Autograd",[5194,20431,20432,20435,20440,20445,20451],{},[5197,20433,20434],{},"Автоматичне обчислення градієнтів через граф обчислень",[5197,20436,20437,20439],{},[5381,20438,16448],{}," увімкнути відстеження операцій",[5197,20441,20442,20444],{},[5381,20443,16189],{}," — запуск backpropagation",[5197,20446,20447,20450],{},[5381,20448,20449],{},".grad"," — доступ до обчислених градієнтів",[5197,20452,20453,20454,20456],{},"Завжди викликайте ",[5381,20455,17945],{}," перед новим backward",[5189,20458,20461],{"icon":20459,"title":20460},"i-lucide-settings","🎛️ Управління градієнтами",[5194,20462,20463,20468,20473,20478],{},[5197,20464,20465,20467],{},[5381,20466,18238],{}," — вимкнути autograd для блоку коду",[5197,20469,20470,20472],{},[5381,20471,18292],{}," — відокремити тензор від графа",[5197,20474,20475,20477],{},[5381,20476,7030],{}," — створити незалежну копію",[5197,20479,20480,20481,20484],{},"In-place операції (",[5381,20482,20483],{},"add_()",") — економія пам'яті",[5189,20486,20489],{"icon":20487,"title":20488},"i-lucide-lightbulb","💡 Практичні кейси",[5194,20490,20491,20497,20500,20503,20506,20509],{},[5197,20492,20493,20494],{},"Нормалізація зображень до ",[5384,20495,20496],{},"-1, 1",[5197,20498,20499],{},"One-hot encoding для класів",[5197,20501,20502],{},"Батчування даних",[5197,20504,20505],{},"Стандартизація датасету",[5197,20507,20508],{},"Градієнтний спуск вручну",[5197,20510,20511],{},"Лінійна регресія з нуля",[20513,20514,20515,20549,20622,20684],"accordion",{},[20516,20517,20520,20526,20546],"accordion-item",{"icon":20518,"label":20519},"i-lucide-help-circle","❓ Чому використовують float32 замість float64?",[5262,20521,20522,20525],{},[5220,20523,20524],{},"Float32 (32-біт)"," є стандартом для нейронних мереж, незважаючи на меншу точність порівняно з float64 (64-біт):",[10217,20527,20528,20534,20540],{},[5197,20529,20530,20533],{},[5220,20531,20532],{},"Економія пам'яті:"," Вдвічі менше пам'яті — можна тренувати більші моделі або використовувати більші батчі.",[5197,20535,20536,20539],{},[5220,20537,20538],{},"Швидкість GPU:"," Сучасні GPU оптимізовані саме для float32. Float64 може бути у 2-10 разів повільніше.",[5197,20541,20542,20545],{},[5220,20543,20544],{},"Достатня точність:"," Для більшості задач ML точність float32 (~7 значущих цифр) цілком достатня. Нейронні мережі стійкі до невеликих похибок.",[5262,20547,20548],{},"Виняток: наукові обчислення з високими вимогами до точності (наприклад, фізичне моделювання).",[20516,20550,20552,20562,20567,20580,20585],{"icon":20518,"label":20551},"❓ Що станеться, якщо забути викликати .zero_grad()?",[5262,20553,20554,20555,20558,20559,20561],{},"Градієнти ",[5220,20556,20557],{},"накопичуються"," з кожним викликом ",[5381,20560,16189],{},". Замість оновлення на основі поточного батча, модель буде оновлюватися на основі суми градієнтів усіх попередніх батчів.",[5262,20563,20564],{},[5220,20565,20566],{},"Результат:",[5194,20568,20569,20572,20575],{},[5197,20570,20571],{},"Нестабільне навчання (loss стрибає)",[5197,20573,20574],{},"Модель не збігається до оптимуму",[5197,20576,20577,20578,5227],{},"Градієнти можуть вибухнути (",[5224,20579,14203],{},[5262,20581,20582],{},[5220,20583,20584],{},"Правильний порядок:",[5374,20586,20588],{"className":5496,"code":20587,"language":5498,"meta":5379,"style":5379},"optimizer.zero_grad()  # 1. Обнулити\nloss = model(X, y)      # 2. Forward\nloss.backward()         # 3. Backward\noptimizer.step()        # 4. Оновити\n",[5381,20589,20590,20598,20606,20614],{"__ignoreMap":5379},[5384,20591,20592,20595],{"class":5386,"line":5387},[5384,20593,20594],{"class":5509},"optimizer.zero_grad()  ",[5384,20596,20597],{"class":6008},"# 1. Обнулити\n",[5384,20599,20600,20603],{"class":5386,"line":5513},[5384,20601,20602],{"class":5509},"loss = model(X, y)      ",[5384,20604,20605],{"class":6008},"# 2. Forward\n",[5384,20607,20608,20611],{"class":5386,"line":5520},[5384,20609,20610],{"class":5509},"loss.backward()         ",[5384,20612,20613],{"class":6008},"# 3. Backward\n",[5384,20615,20616,20619],{"class":5386,"line":5554},[5384,20617,20618],{"class":5509},"optimizer.step()        ",[5384,20620,20621],{"class":6008},"# 4. Оновити\n",[20516,20623,20625,20632,20637,20663,20668,20681],{"icon":20518,"label":20624},"❓ Чому broadcasting корисний для Deep Learning?",[5262,20626,20627,20628,20631],{},"Broadcasting дозволяє ",[5220,20629,20630],{},"векторизувати"," операції, що критично важливо для швидкості:",[5262,20633,20634],{},[5220,20635,20636],{},"Без broadcasting (повільно):",[5374,20638,20640],{"className":5496,"code":20639,"language":5498,"meta":5379,"style":5379},"for i in range(batch_size):\n    output[i] = model(X[i]) + bias  # Цикл по батчу\n",[5381,20641,20642,20655],{"__ignoreMap":5379},[5384,20643,20644,20646,20648,20650,20652],{"class":5386,"line":5387},[5384,20645,14794],{"class":5505},[5384,20647,14797],{"class":5509},[5384,20649,14800],{"class":5505},[5384,20651,14803],{"class":5390},[5384,20653,20654],{"class":5509},"(batch_size):\n",[5384,20656,20657,20660],{"class":5386,"line":5513},[5384,20658,20659],{"class":5509},"    output[i] = model(X[i]) + bias  ",[5384,20661,20662],{"class":6008},"# Цикл по батчу\n",[5262,20664,20665],{},[5220,20666,20667],{},"З broadcasting (швидко):",[5374,20669,20671],{"className":5496,"code":20670,"language":5498,"meta":5379,"style":5379},"output = model(X) + bias  # Одна операція для всього батча\n",[5381,20672,20673],{"__ignoreMap":5379},[5384,20674,20675,20678],{"class":5386,"line":5387},[5384,20676,20677],{"class":5509},"output = model(X) + bias  ",[5384,20679,20680],{"class":6008},"# Одна операція для всього батча\n",[5262,20682,20683],{},"GPU виконують векторні операції у тисячі разів швидше за послідовні цикли. Broadcasting автоматично розширює форми для векторизації.",[20516,20685,20687,20695,20706,20712,20725],{"icon":20518,"label":20686},"❓ Коли використовувати .detach() vs torch.no_grad()?",[5262,20688,20689,20694],{},[5220,20690,20691,20692],{},"Використовуйте ",[5381,20693,18292],{},", коли потрібно:",[5194,20696,20697,20700,20703],{},[5197,20698,20699],{},"Відокремити проміжний результат від графа",[5197,20701,20702],{},"Використати значення як константу у подальших обчисленнях",[5197,20704,20705],{},"Реалізувати техніки на кшталт stop-gradient",[5262,20707,20708,20694],{},[5220,20709,20691,20710],{},[5381,20711,18238],{},[5194,20713,20714,20716,20719,20722],{},[5197,20715,18268],{},[5197,20717,20718],{},"Inference (генерація передбачень)",[5197,20720,20721],{},"Обчислення метрик без графа обчислень",[5197,20723,20724],{},"Економія пам'яті для великих обчислень",[5262,20726,20727,7015,20730,20732,20733,20735],{},[5220,20728,20729],{},"Головна різниця:",[5381,20731,18292],{}," для окремого тензора, ",[5381,20734,18572],{}," для цілого блоку коду.",[5688,20737],{},[5257,20739,20741],{"id":20740},"що-далі","Що далі?",[5262,20743,20744,20745,20748],{},"Тепер ви знаєте основи роботи з тензорами та автоматичного диференціювання — фундамент для будь-яких задач Deep Learning. У наступній статті ",[5220,20746,20747],{},"«Побудова та тренування нейронних мереж у PyTorch»"," ми:",[5194,20750,20751,20758,20761,20764],{},[5197,20752,20753,20754,20757],{},"Познайомимося з модулем ",[5381,20755,20756],{},"nn.Module"," для побудови моделей",[5197,20759,20760],{},"Вивчимо функції втрат та оптимізатори",[5197,20762,20763],{},"Реалізуємо повний цикл тренування від простого до production-ready",[5197,20765,20766],{},"Побудуємо нейронну мережу для класифікації на реальному датасеті",[6588,20768,20769,20772,20773,20776,20777,20780,20781,20784,20785,20788],{},[5220,20770,20771],{},"Практика:"," Спробуйте реалізувати градієнтний спуск для функції ",[15379,20774],{"tex":20775,":inline":15382},"f(x, y) = x^2 + y^2"," з початкової точки ",[5381,20778,20779],{},"(5, 5)"," до мінімуму ",[5381,20782,20783],{},"(0, 0)",". 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Функції активації, прямий та зворотний прохід, оптимізатори.",{"title":3905,"path":3906,"stem":3907,"description":20826,"children":-1},"nn.Module, архітектури моделей, функції втрат, оптимізатори, повний цикл тренування від простого до production-ready.",1791987090052]