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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":3980,"body":5182,"description":15211,"extension":15212,"links":15213,"meta":15214,"navigation":5592,"path":3981,"seo":15215,"stem":3982,"__hash__":15216},"docs\u002F16.ai-python\u002F16.batching-normalization-deep-networks\u002F07.final-project-deep-classifier.md",{"type":5183,"value":5184,"toc":15159},"minimark",[5185,5190,5194,5238,5252,5257,5263,5406,5482,5485,5489,5492,5496,6411,6449,6453,6928,6942,6944,6948,6955,6959,7796,7799,7836,7840,8767,8807,8809,8813,8819,8825,8829,8884,9270,9274,9392,9441,9443,9447,9452,9457,9461,10250,10254,10398,10441,10443,10447,10452,10457,10461,11637,11641,12062,12109,12111,12115,12118,12122,13926,13928,13961,13963,13967,13970,13974,14023,14027,14030,14114,14138,14140,14144,14147,14652,14654,14658,14662,14665,14721,14725,14815,14860,14862,14866,14869,15084,15104,15106,15110,15117,15120,15143,15149,15155],[5186,5187,5189],"h2",{"id":5188},"від-теорії-до-практики-побудова-production-ready-класифікатора","Від теорії до практики — побудова production-ready класифікатора",[5191,5192,5193],"p",{},"Протягом цього модуля ми послідовно опанували ключові техніки глибокого навчання:",[5195,5196,5197,5207,5221,5225,5229,5233],"card-group",{},[5198,5199,5201,5202,5206],"card",{"icon":2865,"title":5200},"1️⃣ Mini-batch Gradient Descent","Навчилися ефективно обробляти великі датасети через батчі, розуміємо різницю між Batch GD, SGD та Mini-batch GD. Використовуємо PyTorch ",[5203,5204,5205],"code",{},"DataLoader"," для паралельного завантаження.",[5198,5208,5211,5212,5216,5217,5220],{"icon":5209,"title":5210},"i-lucide-settings","2️⃣ Weight Initialization","Опанували правильну ініціалізацію ваг: ",[5213,5214,5215],"strong",{},"Xavier"," для tanh\u002Fsigmoid, ",[5213,5218,5219],{},"He"," для ReLU. Розуміємо проблеми vanishing\u002Fexploding gradients та як ініціалізація їх вирішує.",[5198,5222,5224],{"icon":92,"title":5223},"3️⃣ Batch Normalization","Стабілізуємо навчання через нормалізацію активацій після кожного Conv шару. Розуміємо різницю train\u002Feval режимів та running statistics.",[5198,5226,5228],{"icon":4660,"title":5227},"4️⃣ Dropout & Regularization","Боремося з перенавчанням через випадкове вимикання нейронів, weight decay та data augmentation. Знаємо, коли і як комбінувати техніки.",[5198,5230,5232],{"icon":943,"title":5231},"5️⃣ Deep Networks (ResNet)","Будуємо дуже глибокі мережі через Skip Connections. Розуміємо degradation problem та як residual learning його вирішує.",[5198,5234,5237],{"icon":5235,"title":5236},"i-lucide-trending-down","6️⃣ Learning Rate Scheduling","Адаптивно змінюємо швидкість навчання через Cosine Annealing, Warm-up та інші стратегії. Досягаємо +9% accuracy тільки через правильний scheduler!",[5191,5239,5240,5243,5244,5247,5248,5251],{},[5213,5241,5242],{},"Тепер настав час об'єднати все разом!"," У цій фінальній статті ми побудуємо ",[5213,5245,5246],{},"production-ready класифікатор зображень"," на датасеті ",[5213,5249,5250],{},"CIFAR-100",", проходячи через ітеративний процес покращень від baseline до state-of-the-art моделі.",[5253,5254,5256],"h3",{"id":5255},"чому-cifar-100-а-не-cifar-10","Чому CIFAR-100, а не CIFAR-10?",[5191,5258,5259,5260,5262],{},"У попередніх статтях ми працювали з CIFAR-10 (10 класів: літак, авто, птах тощо). Для фінального проєкту використаємо ",[5213,5261,5250],{}," — значно складнішу задачу:",[5195,5264,5265,5368],{},[5198,5266,5269],{"icon":5267,"title":5268},"i-lucide-bar-chart","📊 CIFAR-10 vs CIFAR-100",[5270,5271,5272,5288],"table",{},[5273,5274,5275],"thead",{},[5276,5277,5278,5283,5286],"tr",{},[5279,5280,5282],"th",{"align":5281},"left","Параметр",[5279,5284,5285],{"align":5281},"CIFAR-10",[5279,5287,5250],{"align":5281},[5289,5290,5291,5307,5323,5338,5353],"tbody",{},[5276,5292,5293,5299,5302],{},[5294,5295,5296],"td",{"align":5281},[5213,5297,5298],{},"Кількість класів",[5294,5300,5301],{"align":5281},"10",[5294,5303,5304],{"align":5281},[5213,5305,5306],{},"100",[5276,5308,5309,5314,5317],{},[5294,5310,5311],{"align":5281},[5213,5312,5313],{},"Приклади на клас",[5294,5315,5316],{"align":5281},"5,000 (train)",[5294,5318,5319,5322],{"align":5281},[5213,5320,5321],{},"500"," (train)",[5276,5324,5325,5330,5333],{},[5294,5326,5327],{"align":5281},[5213,5328,5329],{},"Складність",[5294,5331,5332],{"align":5281},"Легко",[5294,5334,5335],{"align":5281},[5213,5336,5337],{},"Реалістична",[5276,5339,5340,5345,5348],{},[5294,5341,5342],{"align":5281},[5213,5343,5344],{},"Human accuracy",[5294,5346,5347],{"align":5281},"~94%",[5294,5349,5350],{"align":5281},[5213,5351,5352],{},"~75%",[5276,5354,5355,5360,5363],{},[5294,5356,5357],{"align":5281},[5213,5358,5359],{},"Random baseline",[5294,5361,5362],{"align":5281},"10%",[5294,5364,5365],{"align":5281},[5213,5366,5367],{},"1%",[5198,5369,5372,5377,5399],{"icon":5370,"title":5371},"i-lucide-target","🎯 Чому це важливо",[5191,5373,5374,5376],{},[5213,5375,5250],{}," наближена до реальних промислових задач:",[5378,5379,5380,5387,5393],"ul",{},[5381,5382,5383,5386],"li",{},[5213,5384,5385],{},"Малий датасет"," на клас (500 зображень) → ризик перенавчання",[5381,5388,5389,5392],{},[5213,5390,5391],{},"100 класів"," → потрібна потужна модель",[5381,5394,5395,5398],{},[5213,5396,5397],{},"Схожі класи"," (5 типів риб, 10 тварин) → важко розрізнити",[5191,5400,5401,5402,5405],{},"Це ідеальне середовище для застосування ",[5213,5403,5404],{},"всіх"," вивчених технік!",[5195,5407,5408,5438],{},[5198,5409,5411,5420,5427],{"icon":5370,"title":5410},"🎯 Мета проєкту",[5191,5412,5413,5416,5417],{},[5213,5414,5415],{},"Цільова метрика:"," Test Accuracy ",[5213,5418,5419],{},"> 72%",[5191,5421,5422,5423,5426],{},"Ми почнемо з простого baseline (~45%) та ",[5213,5424,5425],{},"ітеративно покращуватимемо"," модель, застосовуючи техніки одну за одною. Кожне покращення супроводжуватиметься:",[5378,5428,5429,5432,5435],{},[5381,5430,5431],{},"Конкретним код-snippetом",[5381,5433,5434],{},"Результатами тренування",[5381,5436,5437],{},"Аналізом: чому це спрацювало і на скільки",[5198,5439,5442,5475],{"icon":5440,"title":5441},"i-lucide-list-checks","📋 План ітерацій",[5443,5444,5445,5451,5457,5463,5469],"ol",{},[5381,5446,5447,5450],{},[5213,5448,5449],{},"Baseline"," — проста CNN без оптимізацій (~45% acc)",[5381,5452,5453,5456],{},[5213,5454,5455],{},"+ Data Augmentation"," — RandomCrop, HorizontalFlip (~55%)",[5381,5458,5459,5462],{},[5213,5460,5461],{},"+ BatchNorm + Dropout"," — стабілізація та регуляризація (~62%)",[5381,5464,5465,5468],{},[5213,5466,5467],{},"+ Residual Connections"," — ResNet архітектура (~68%)",[5381,5470,5471,5474],{},[5213,5472,5473],{},"+ LR Scheduling + Weight Decay"," — fine-tuning (~72%+)",[5191,5476,5477,5478,5481],{},"Фінальна модель досягне ",[5213,5479,5480],{},">72% accuracy"," — на рівні сучасних публікацій!",[5483,5484],"hr",{},[5186,5486,5488],{"id":5487},"аналіз-датасету-cifar-100","Аналіз датасету CIFAR-100",[5191,5490,5491],{},"Перш ніж почати будувати модель, детально вивчимо датасет. Розуміння даних — перший крок до успішного ML проєкту.",[5253,5493,5495],{"id":5494},"завантаження-та-структура","Завантаження та структура",[5497,5498,5501,5512,6117,6126],"jupyter-notebook",{"kernel":5499,"title":5500},"Python 3.11 (ipykernel)","cifar100_exploration.ipynb",[5502,5503,5505,5509],"jupyter-cell",{"type":5504},"markdown",[5253,5506,5508],{"id":5507},"завантаження-cifar-100-та-базова-статистика","Завантаження CIFAR-100 та базова статистика",[5191,5510,5511],{},"CIFAR-100 має 100 класів, організованих у 20 superclasses (категорій вищого рівня).",[5502,5513,5516,6015],{"execution-count":5514,"execution-time":5515},"1","2.5s",[5517,5518,5523],"pre",{"className":5519,"code":5520,"language":5521,"meta":5522,"style":5522},"language-python shiki shiki-themes light-plus dark-plus dark-plus","import torch\nimport torchvision\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Завантаження датасету\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n])\n\ntrain_dataset = datasets.CIFAR100(root='.\u002Fdata', train=True, download=True, transform=transform)\ntest_dataset = datasets.CIFAR100(root='.\u002Fdata', train=False, download=True, transform=transform)\n\nprint(\"📊 Статистика CIFAR-100:\\n\")\nprint(f\"├─ Тренувальних зображень: {len(train_dataset):,}\")\nprint(f\"├─ Тестових зображень: {len(test_dataset):,}\")\nprint(f\"├─ Кількість класів: {len(train_dataset.classes)}\")\nprint(f\"├─ Форма зображення: {train_dataset[0][0].shape}\")\nprint(f\"└─ Приклади на клас (train): {len(train_dataset) \u002F\u002F len(train_dataset.classes)}\")\n\n# Перші 20 класів (для демонстрації)\nprint(f\"\\n📋 Перші 20 класів (з 100):\")\nfor i, class_name in enumerate(train_dataset.classes[:20]):\n    print(f\"  {i:2d}. {class_name}\")\nprint(\"  ...\")\n","python","",[5203,5524,5525,5538,5546,5560,5574,5587,5594,5601,5607,5613,5653,5659,5664,5709,5744,5749,5772,5801,5826,5852,5886,5915,5920,5926,5944,5968,6003],{"__ignoreMap":5522},[5526,5527,5530,5534],"span",{"class":5528,"line":5529},"line",1,[5526,5531,5533],{"class":5532},"s8xlr","import",[5526,5535,5537],{"class":5536},"sHH4Y"," torch\n",[5526,5539,5541,5543],{"class":5528,"line":5540},2,[5526,5542,5533],{"class":5532},[5526,5544,5545],{"class":5536}," torchvision\n",[5526,5547,5549,5552,5555,5557],{"class":5528,"line":5548},3,[5526,5550,5551],{"class":5532},"from",[5526,5553,5554],{"class":5536}," torchvision ",[5526,5556,5533],{"class":5532},[5526,5558,5559],{"class":5536}," datasets, transforms\n",[5526,5561,5563,5565,5568,5571],{"class":5528,"line":5562},4,[5526,5564,5533],{"class":5532},[5526,5566,5567],{"class":5536}," matplotlib.pyplot ",[5526,5569,5570],{"class":5532},"as",[5526,5572,5573],{"class":5536}," plt\n",[5526,5575,5577,5579,5582,5584],{"class":5528,"line":5576},5,[5526,5578,5533],{"class":5532},[5526,5580,5581],{"class":5536}," numpy ",[5526,5583,5570],{"class":5532},[5526,5585,5586],{"class":5536}," np\n",[5526,5588,5590],{"class":5528,"line":5589},6,[5526,5591,5593],{"emptyLinePlaceholder":5592},true,"\n",[5526,5595,5597],{"class":5528,"line":5596},7,[5526,5598,5600],{"class":5599},"spJ8K","# Завантаження датасету\n",[5526,5602,5604],{"class":5528,"line":5603},8,[5526,5605,5606],{"class":5536},"transform = transforms.Compose([\n",[5526,5608,5610],{"class":5528,"line":5609},9,[5526,5611,5612],{"class":5536},"    transforms.ToTensor(),\n",[5526,5614,5616,5619,5623,5626,5629,5631,5634,5637,5640,5642,5645,5647,5650],{"class":5528,"line":5615},10,[5526,5617,5618],{"class":5536},"    transforms.Normalize((",[5526,5620,5622],{"class":5621},"sJj4R","0.5071",[5526,5624,5625],{"class":5536},", ",[5526,5627,5628],{"class":5621},"0.4867",[5526,5630,5625],{"class":5536},[5526,5632,5633],{"class":5621},"0.4408",[5526,5635,5636],{"class":5536},"), (",[5526,5638,5639],{"class":5621},"0.2675",[5526,5641,5625],{"class":5536},[5526,5643,5644],{"class":5621},"0.2565",[5526,5646,5625],{"class":5536},[5526,5648,5649],{"class":5621},"0.2761",[5526,5651,5652],{"class":5536},"))\n",[5526,5654,5656],{"class":5528,"line":5655},11,[5526,5657,5658],{"class":5536},"])\n",[5526,5660,5662],{"class":5528,"line":5661},12,[5526,5663,5593],{"emptyLinePlaceholder":5592},[5526,5665,5667,5670,5674,5677,5681,5683,5686,5688,5692,5694,5697,5699,5701,5703,5706],{"class":5528,"line":5666},13,[5526,5668,5669],{"class":5536},"train_dataset = datasets.CIFAR100(",[5526,5671,5673],{"class":5672},"siwwj","root",[5526,5675,5676],{"class":5536},"=",[5526,5678,5680],{"class":5679},"sbdoH","'.\u002Fdata'",[5526,5682,5625],{"class":5536},[5526,5684,5685],{"class":5672},"train",[5526,5687,5676],{"class":5536},[5526,5689,5691],{"class":5690},"su1O8","True",[5526,5693,5625],{"class":5536},[5526,5695,5696],{"class":5672},"download",[5526,5698,5676],{"class":5536},[5526,5700,5691],{"class":5690},[5526,5702,5625],{"class":5536},[5526,5704,5705],{"class":5672},"transform",[5526,5707,5708],{"class":5536},"=transform)\n",[5526,5710,5712,5715,5717,5719,5721,5723,5725,5727,5730,5732,5734,5736,5738,5740,5742],{"class":5528,"line":5711},14,[5526,5713,5714],{"class":5536},"test_dataset = datasets.CIFAR100(",[5526,5716,5673],{"class":5672},[5526,5718,5676],{"class":5536},[5526,5720,5680],{"class":5679},[5526,5722,5625],{"class":5536},[5526,5724,5685],{"class":5672},[5526,5726,5676],{"class":5536},[5526,5728,5729],{"class":5690},"False",[5526,5731,5625],{"class":5536},[5526,5733,5696],{"class":5672},[5526,5735,5676],{"class":5536},[5526,5737,5691],{"class":5690},[5526,5739,5625],{"class":5536},[5526,5741,5705],{"class":5672},[5526,5743,5708],{"class":5536},[5526,5745,5747],{"class":5528,"line":5746},15,[5526,5748,5593],{"emptyLinePlaceholder":5592},[5526,5750,5752,5756,5759,5762,5766,5769],{"class":5528,"line":5751},16,[5526,5753,5755],{"class":5754},"s8Opu","print",[5526,5757,5758],{"class":5536},"(",[5526,5760,5761],{"class":5679},"\"📊 Статистика CIFAR-100:",[5526,5763,5765],{"class":5764},"sjcCO","\\n",[5526,5767,5768],{"class":5679},"\"",[5526,5770,5771],{"class":5536},")\n",[5526,5773,5775,5777,5779,5782,5785,5788,5791,5794,5797,5799],{"class":5528,"line":5774},17,[5526,5776,5755],{"class":5754},[5526,5778,5758],{"class":5536},[5526,5780,5781],{"class":5690},"f",[5526,5783,5784],{"class":5679},"\"├─ Тренувальних зображень: ",[5526,5786,5787],{"class":5690},"{",[5526,5789,5790],{"class":5754},"len",[5526,5792,5793],{"class":5536},"(train_dataset)",[5526,5795,5796],{"class":5690},":,}",[5526,5798,5768],{"class":5679},[5526,5800,5771],{"class":5536},[5526,5802,5804,5806,5808,5810,5813,5815,5817,5820,5822,5824],{"class":5528,"line":5803},18,[5526,5805,5755],{"class":5754},[5526,5807,5758],{"class":5536},[5526,5809,5781],{"class":5690},[5526,5811,5812],{"class":5679},"\"├─ Тестових зображень: ",[5526,5814,5787],{"class":5690},[5526,5816,5790],{"class":5754},[5526,5818,5819],{"class":5536},"(test_dataset)",[5526,5821,5796],{"class":5690},[5526,5823,5768],{"class":5679},[5526,5825,5771],{"class":5536},[5526,5827,5829,5831,5833,5835,5838,5840,5842,5845,5848,5850],{"class":5528,"line":5828},19,[5526,5830,5755],{"class":5754},[5526,5832,5758],{"class":5536},[5526,5834,5781],{"class":5690},[5526,5836,5837],{"class":5679},"\"├─ Кількість класів: ",[5526,5839,5787],{"class":5690},[5526,5841,5790],{"class":5754},[5526,5843,5844],{"class":5536},"(train_dataset.classes)",[5526,5846,5847],{"class":5690},"}",[5526,5849,5768],{"class":5679},[5526,5851,5771],{"class":5536},[5526,5853,5855,5857,5859,5861,5864,5866,5869,5872,5875,5877,5880,5882,5884],{"class":5528,"line":5854},20,[5526,5856,5755],{"class":5754},[5526,5858,5758],{"class":5536},[5526,5860,5781],{"class":5690},[5526,5862,5863],{"class":5679},"\"├─ Форма зображення: ",[5526,5865,5787],{"class":5690},[5526,5867,5868],{"class":5536},"train_dataset[",[5526,5870,5871],{"class":5621},"0",[5526,5873,5874],{"class":5536},"][",[5526,5876,5871],{"class":5621},[5526,5878,5879],{"class":5536},"].shape",[5526,5881,5847],{"class":5690},[5526,5883,5768],{"class":5679},[5526,5885,5771],{"class":5536},[5526,5887,5889,5891,5893,5895,5898,5900,5902,5905,5907,5909,5911,5913],{"class":5528,"line":5888},21,[5526,5890,5755],{"class":5754},[5526,5892,5758],{"class":5536},[5526,5894,5781],{"class":5690},[5526,5896,5897],{"class":5679},"\"└─ Приклади на клас (train): ",[5526,5899,5787],{"class":5690},[5526,5901,5790],{"class":5754},[5526,5903,5904],{"class":5536},"(train_dataset) \u002F\u002F ",[5526,5906,5790],{"class":5754},[5526,5908,5844],{"class":5536},[5526,5910,5847],{"class":5690},[5526,5912,5768],{"class":5679},[5526,5914,5771],{"class":5536},[5526,5916,5918],{"class":5528,"line":5917},22,[5526,5919,5593],{"emptyLinePlaceholder":5592},[5526,5921,5923],{"class":5528,"line":5922},23,[5526,5924,5925],{"class":5599},"# Перші 20 класів (для демонстрації)\n",[5526,5927,5929,5931,5933,5935,5937,5939,5942],{"class":5528,"line":5928},24,[5526,5930,5755],{"class":5754},[5526,5932,5758],{"class":5536},[5526,5934,5781],{"class":5690},[5526,5936,5768],{"class":5679},[5526,5938,5765],{"class":5764},[5526,5940,5941],{"class":5679},"📋 Перші 20 класів (з 100):\"",[5526,5943,5771],{"class":5536},[5526,5945,5947,5950,5953,5956,5959,5962,5965],{"class":5528,"line":5946},25,[5526,5948,5949],{"class":5532},"for",[5526,5951,5952],{"class":5536}," i, class_name ",[5526,5954,5955],{"class":5532},"in",[5526,5957,5958],{"class":5754}," enumerate",[5526,5960,5961],{"class":5536},"(train_dataset.classes[:",[5526,5963,5964],{"class":5621},"20",[5526,5966,5967],{"class":5536},"]):\n",[5526,5969,5971,5974,5976,5978,5981,5983,5986,5989,5992,5994,5997,5999,6001],{"class":5528,"line":5970},26,[5526,5972,5973],{"class":5754},"    print",[5526,5975,5758],{"class":5536},[5526,5977,5781],{"class":5690},[5526,5979,5980],{"class":5679},"\"  ",[5526,5982,5787],{"class":5690},[5526,5984,5985],{"class":5536},"i",[5526,5987,5988],{"class":5690},":2d}",[5526,5990,5991],{"class":5679},". ",[5526,5993,5787],{"class":5690},[5526,5995,5996],{"class":5536},"class_name",[5526,5998,5847],{"class":5690},[5526,6000,5768],{"class":5679},[5526,6002,5771],{"class":5536},[5526,6004,6006,6008,6010,6013],{"class":5528,"line":6005},27,[5526,6007,5755],{"class":5754},[5526,6009,5758],{"class":5536},[5526,6011,6012],{"class":5679},"\"  ...\"",[5526,6014,5771],{"class":5536},[6016,6017,6018,6021,6027,6051,6058],"template",{"v-slot:output":5522},[5191,6019,6020],{},"Downloading CIFAR-100 dataset...\n✅ Завершено",[5191,6022,6023,6024],{},"📊 ",[5213,6025,6026],{},"Статистика CIFAR-100:",[5378,6028,6029,6032,6035,6038,6045],{},[5381,6030,6031],{},"Тренувальних зображень: 50,000",[5381,6033,6034],{},"Тестових зображень: 10,000",[5381,6036,6037],{},"Кількість класів: 100",[5381,6039,6040,6041,6044],{},"Форма зображення: ",[5203,6042,6043],{},"torch.Size([3, 32, 32])"," (RGB, 32×32 пікселів)",[5381,6046,6047,6048,6050],{},"Приклади на клас (train): ",[5213,6049,5321],{}," (мало!)",[5191,6052,6053,6054,6057],{},"📋 ",[5213,6055,6056],{},"Перші 20 класів (з 100):","\n0. apple",[5443,6059,6060,6063,6066,6069,6072,6075,6078,6081,6084,6087,6090,6093,6096,6099,6102,6105,6108,6111,6114],{},[5381,6061,6062],{},"aquarium_fish",[5381,6064,6065],{},"baby",[5381,6067,6068],{},"bear",[5381,6070,6071],{},"beaver",[5381,6073,6074],{},"bed",[5381,6076,6077],{},"bee",[5381,6079,6080],{},"beetle",[5381,6082,6083],{},"bicycle",[5381,6085,6086],{},"bottle",[5381,6088,6089],{},"bowl",[5381,6091,6092],{},"boy",[5381,6094,6095],{},"bridge",[5381,6097,6098],{},"bus",[5381,6100,6101],{},"butterfly",[5381,6103,6104],{},"camel",[5381,6106,6107],{},"can",[5381,6109,6110],{},"castle",[5381,6112,6113],{},"caterpillar",[5381,6115,6116],{},"cattle\n...",[5502,6118,6119,6123],{"type":5504},[5253,6120,6122],{"id":6121},"візуалізація-прикладів-із-різних-класів","Візуалізація прикладів із різних класів",[5191,6124,6125],{},"Подивимося на реальні зображення та їхню якість.",[5502,6127,6130,6402],{"execution-count":6128,"execution-time":6129},"2","1.2s",[5517,6131,6133],{"className":5519,"code":6132,"language":5521,"meta":5522,"style":5522},"# Візуалізація випадкових прикладів\nfig, axes = plt.subplots(4, 8, figsize=(16, 8))\naxes = axes.flatten()\n\n# Випадково обираємо 32 зображення\nnp.random.seed(42)\nindices = np.random.choice(len(train_dataset), size=32, replace=False)\n\nfor idx, ax in zip(indices, axes):\n    image, label = train_dataset[idx]\n    \n    # Денормалізація для візуалізації\n    image = image.numpy().transpose(1, 2, 0)\n    mean = np.array([0.5071, 0.4867, 0.4408])\n    std = np.array([0.2675, 0.2565, 0.2761])\n    image = image * std + mean\n    image = np.clip(image, 0, 1)\n    \n    ax.imshow(image)\n    ax.set_title(train_dataset.classes[label], fontsize=8)\n    ax.axis('off')\n\nplt.suptitle('CIFAR-100: Випадкові приклади з різних класів', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.show()\n",[5203,6134,6135,6140,6170,6175,6179,6184,6194,6223,6227,6242,6247,6252,6257,6274,6291,6308,6313,6326,6330,6335,6349,6359,6363,6392,6397],{"__ignoreMap":5522},[5526,6136,6137],{"class":5528,"line":5529},[5526,6138,6139],{"class":5599},"# Візуалізація випадкових прикладів\n",[5526,6141,6142,6145,6148,6150,6153,6155,6158,6161,6164,6166,6168],{"class":5528,"line":5540},[5526,6143,6144],{"class":5536},"fig, axes = plt.subplots(",[5526,6146,6147],{"class":5621},"4",[5526,6149,5625],{"class":5536},[5526,6151,6152],{"class":5621},"8",[5526,6154,5625],{"class":5536},[5526,6156,6157],{"class":5672},"figsize",[5526,6159,6160],{"class":5536},"=(",[5526,6162,6163],{"class":5621},"16",[5526,6165,5625],{"class":5536},[5526,6167,6152],{"class":5621},[5526,6169,5652],{"class":5536},[5526,6171,6172],{"class":5528,"line":5548},[5526,6173,6174],{"class":5536},"axes = axes.flatten()\n",[5526,6176,6177],{"class":5528,"line":5562},[5526,6178,5593],{"emptyLinePlaceholder":5592},[5526,6180,6181],{"class":5528,"line":5576},[5526,6182,6183],{"class":5599},"# Випадково обираємо 32 зображення\n",[5526,6185,6186,6189,6192],{"class":5528,"line":5589},[5526,6187,6188],{"class":5536},"np.random.seed(",[5526,6190,6191],{"class":5621},"42",[5526,6193,5771],{"class":5536},[5526,6195,6196,6199,6201,6204,6207,6209,6212,6214,6217,6219,6221],{"class":5528,"line":5596},[5526,6197,6198],{"class":5536},"indices = np.random.choice(",[5526,6200,5790],{"class":5754},[5526,6202,6203],{"class":5536},"(train_dataset), ",[5526,6205,6206],{"class":5672},"size",[5526,6208,5676],{"class":5536},[5526,6210,6211],{"class":5621},"32",[5526,6213,5625],{"class":5536},[5526,6215,6216],{"class":5672},"replace",[5526,6218,5676],{"class":5536},[5526,6220,5729],{"class":5690},[5526,6222,5771],{"class":5536},[5526,6224,6225],{"class":5528,"line":5603},[5526,6226,5593],{"emptyLinePlaceholder":5592},[5526,6228,6229,6231,6234,6236,6239],{"class":5528,"line":5609},[5526,6230,5949],{"class":5532},[5526,6232,6233],{"class":5536}," idx, ax ",[5526,6235,5955],{"class":5532},[5526,6237,6238],{"class":5754}," zip",[5526,6240,6241],{"class":5536},"(indices, axes):\n",[5526,6243,6244],{"class":5528,"line":5615},[5526,6245,6246],{"class":5536},"    image, label = train_dataset[idx]\n",[5526,6248,6249],{"class":5528,"line":5655},[5526,6250,6251],{"class":5536},"    \n",[5526,6253,6254],{"class":5528,"line":5661},[5526,6255,6256],{"class":5599},"    # Денормалізація для візуалізації\n",[5526,6258,6259,6262,6264,6266,6268,6270,6272],{"class":5528,"line":5666},[5526,6260,6261],{"class":5536},"    image = image.numpy().transpose(",[5526,6263,5514],{"class":5621},[5526,6265,5625],{"class":5536},[5526,6267,6128],{"class":5621},[5526,6269,5625],{"class":5536},[5526,6271,5871],{"class":5621},[5526,6273,5771],{"class":5536},[5526,6275,6276,6279,6281,6283,6285,6287,6289],{"class":5528,"line":5711},[5526,6277,6278],{"class":5536},"    mean = np.array([",[5526,6280,5622],{"class":5621},[5526,6282,5625],{"class":5536},[5526,6284,5628],{"class":5621},[5526,6286,5625],{"class":5536},[5526,6288,5633],{"class":5621},[5526,6290,5658],{"class":5536},[5526,6292,6293,6296,6298,6300,6302,6304,6306],{"class":5528,"line":5746},[5526,6294,6295],{"class":5536},"    std = np.array([",[5526,6297,5639],{"class":5621},[5526,6299,5625],{"class":5536},[5526,6301,5644],{"class":5621},[5526,6303,5625],{"class":5536},[5526,6305,5649],{"class":5621},[5526,6307,5658],{"class":5536},[5526,6309,6310],{"class":5528,"line":5751},[5526,6311,6312],{"class":5536},"    image = image * std + mean\n",[5526,6314,6315,6318,6320,6322,6324],{"class":5528,"line":5774},[5526,6316,6317],{"class":5536},"    image = np.clip(image, ",[5526,6319,5871],{"class":5621},[5526,6321,5625],{"class":5536},[5526,6323,5514],{"class":5621},[5526,6325,5771],{"class":5536},[5526,6327,6328],{"class":5528,"line":5803},[5526,6329,6251],{"class":5536},[5526,6331,6332],{"class":5528,"line":5828},[5526,6333,6334],{"class":5536},"    ax.imshow(image)\n",[5526,6336,6337,6340,6343,6345,6347],{"class":5528,"line":5854},[5526,6338,6339],{"class":5536},"    ax.set_title(train_dataset.classes[label], ",[5526,6341,6342],{"class":5672},"fontsize",[5526,6344,5676],{"class":5536},[5526,6346,6152],{"class":5621},[5526,6348,5771],{"class":5536},[5526,6350,6351,6354,6357],{"class":5528,"line":5888},[5526,6352,6353],{"class":5536},"    ax.axis(",[5526,6355,6356],{"class":5679},"'off'",[5526,6358,5771],{"class":5536},[5526,6360,6361],{"class":5528,"line":5917},[5526,6362,5593],{"emptyLinePlaceholder":5592},[5526,6364,6365,6368,6371,6373,6375,6377,6380,6382,6385,6387,6390],{"class":5528,"line":5922},[5526,6366,6367],{"class":5536},"plt.suptitle(",[5526,6369,6370],{"class":5679},"'CIFAR-100: Випадкові приклади з різних класів'",[5526,6372,5625],{"class":5536},[5526,6374,6342],{"class":5672},[5526,6376,5676],{"class":5536},[5526,6378,6379],{"class":5621},"14",[5526,6381,5625],{"class":5536},[5526,6383,6384],{"class":5672},"fontweight",[5526,6386,5676],{"class":5536},[5526,6388,6389],{"class":5679},"'bold'",[5526,6391,5771],{"class":5536},[5526,6393,6394],{"class":5528,"line":5928},[5526,6395,6396],{"class":5536},"plt.tight_layout()\n",[5526,6398,6399],{"class":5528,"line":5946},[5526,6400,6401],{"class":5536},"plt.show()\n",[6016,6403,6404],{"v-slot:output":5522},[6405,6406],"img",{"alt":6407,"className":6408,"src":6410},"CIFAR-100: 32 випадкових зображення з різних класів",[6409],"diagram-img","\u002Fimages\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Ffinal-project\u002F01.png",[6412,6413,6414,6419],"note",{},[5191,6415,6416],{},[5213,6417,6418],{},"Спостереження про датасет:",[5443,6420,6421,6427,6433,6439],{},[5381,6422,6423,6426],{},[5213,6424,6425],{},"Низька роздільність:"," 32×32 пікселів — дуже мало деталей. Людині важко розрізнити деякі класи.",[5381,6428,6429,6432],{},[5213,6430,6431],{},"Міжкласова схожість:"," Багато класів схожі візуально (наприклад, різні види риб, комах, транспорту). Це ускладнює задачу.",[5381,6434,6435,6438],{},[5213,6436,6437],{},"Внутрішньокласова варіація:"," Навіть у межах одного класу зображення дуже різні (ракурси, освітлення, масштаб).",[5381,6440,6441,6444,6445,6448],{},[5213,6442,6443],{},"Лише 500 прикладів на клас:"," Це ",[5213,6446,6447],{},"мало"," для навчання глибокої мережі. Ризик перенавчання високий! Потрібна агресивна регуляризація та data augmentation.",[5253,6450,6452],{"id":6451},"розподіл-класів-та-balance","Розподіл класів та balance",[5502,6454,6456,6896],{"execution-count":6455},"3",[5517,6457,6459],{"className":5519,"code":6458,"language":5521,"meta":5522,"style":5522},"# Перевірка балансу датасету\nfrom collections import Counter\n\ntrain_labels = [label for _, label in train_dataset]\nclass_counts = Counter(train_labels)\n\nprint(\"📊 Баланс датасету:\")\nprint(f\"├─ Мінімум прикладів у класі: {min(class_counts.values())}\")\nprint(f\"├─ Максимум прикладів у класі: {max(class_counts.values())}\")\nprint(f\"└─ Середнє прикладів на клас: {np.mean(list(class_counts.values())):.1f}\")\n\n# Візуалізація розподілу (перші 50 класів для компактності)\nfig, ax = plt.subplots(figsize=(14, 4))\nclasses = sorted(class_counts.keys())[:50]\ncounts = [class_counts[c] for c in classes]\n\nax.bar(classes, counts, color='#3b82f6', alpha=0.7, edgecolor='black')\nax.axhline(y=500, color='red', linestyle='--', linewidth=2, label='Очікуване (500)')\nax.set_xlabel('Клас (ID)', fontsize=12)\nax.set_ylabel('Кількість зображень', fontsize=12)\nax.set_title('Розподіл прикладів по класах (перші 50)', fontsize=14, fontweight='bold')\nax.legend()\nax.grid(alpha=0.3)\nplt.tight_layout()\nplt.show()\n\nif max(class_counts.values()) == min(class_counts.values()):\n    print(\"\\n✅ Датасет ідеально збалансований! Кожен клас має рівно 500 прикладів.\")\nelse:\n    print(\"\\n⚠️ Датасет незбалансований!\")\n",[5203,6460,6461,6466,6478,6482,6497,6502,6506,6517,6542,6566,6596,6600,6605,6622,6639,6654,6658,6693,6745,6764,6782,6808,6813,6827,6831,6835,6839,6855,6871,6880],{"__ignoreMap":5522},[5526,6462,6463],{"class":5528,"line":5529},[5526,6464,6465],{"class":5599},"# Перевірка балансу датасету\n",[5526,6467,6468,6470,6473,6475],{"class":5528,"line":5540},[5526,6469,5551],{"class":5532},[5526,6471,6472],{"class":5536}," collections ",[5526,6474,5533],{"class":5532},[5526,6476,6477],{"class":5536}," Counter\n",[5526,6479,6480],{"class":5528,"line":5548},[5526,6481,5593],{"emptyLinePlaceholder":5592},[5526,6483,6484,6487,6489,6492,6494],{"class":5528,"line":5562},[5526,6485,6486],{"class":5536},"train_labels = [label ",[5526,6488,5949],{"class":5532},[5526,6490,6491],{"class":5536}," _, label ",[5526,6493,5955],{"class":5532},[5526,6495,6496],{"class":5536}," train_dataset]\n",[5526,6498,6499],{"class":5528,"line":5576},[5526,6500,6501],{"class":5536},"class_counts = Counter(train_labels)\n",[5526,6503,6504],{"class":5528,"line":5589},[5526,6505,5593],{"emptyLinePlaceholder":5592},[5526,6507,6508,6510,6512,6515],{"class":5528,"line":5596},[5526,6509,5755],{"class":5754},[5526,6511,5758],{"class":5536},[5526,6513,6514],{"class":5679},"\"📊 Баланс датасету:\"",[5526,6516,5771],{"class":5536},[5526,6518,6519,6521,6523,6525,6528,6530,6533,6536,6538,6540],{"class":5528,"line":5603},[5526,6520,5755],{"class":5754},[5526,6522,5758],{"class":5536},[5526,6524,5781],{"class":5690},[5526,6526,6527],{"class":5679},"\"├─ Мінімум прикладів у класі: ",[5526,6529,5787],{"class":5690},[5526,6531,6532],{"class":5754},"min",[5526,6534,6535],{"class":5536},"(class_counts.values())",[5526,6537,5847],{"class":5690},[5526,6539,5768],{"class":5679},[5526,6541,5771],{"class":5536},[5526,6543,6544,6546,6548,6550,6553,6555,6558,6560,6562,6564],{"class":5528,"line":5609},[5526,6545,5755],{"class":5754},[5526,6547,5758],{"class":5536},[5526,6549,5781],{"class":5690},[5526,6551,6552],{"class":5679},"\"├─ Максимум прикладів у класі: ",[5526,6554,5787],{"class":5690},[5526,6556,6557],{"class":5754},"max",[5526,6559,6535],{"class":5536},[5526,6561,5847],{"class":5690},[5526,6563,5768],{"class":5679},[5526,6565,5771],{"class":5536},[5526,6567,6568,6570,6572,6574,6577,6579,6582,6586,6589,6592,6594],{"class":5528,"line":5615},[5526,6569,5755],{"class":5754},[5526,6571,5758],{"class":5536},[5526,6573,5781],{"class":5690},[5526,6575,6576],{"class":5679},"\"└─ Середнє прикладів на клас: ",[5526,6578,5787],{"class":5690},[5526,6580,6581],{"class":5536},"np.mean(",[5526,6583,6585],{"class":6584},"sN1BT","list",[5526,6587,6588],{"class":5536},"(class_counts.values()))",[5526,6590,6591],{"class":5690},":.1f}",[5526,6593,5768],{"class":5679},[5526,6595,5771],{"class":5536},[5526,6597,6598],{"class":5528,"line":5655},[5526,6599,5593],{"emptyLinePlaceholder":5592},[5526,6601,6602],{"class":5528,"line":5661},[5526,6603,6604],{"class":5599},"# Візуалізація розподілу (перші 50 класів для компактності)\n",[5526,6606,6607,6610,6612,6614,6616,6618,6620],{"class":5528,"line":5666},[5526,6608,6609],{"class":5536},"fig, ax = plt.subplots(",[5526,6611,6157],{"class":5672},[5526,6613,6160],{"class":5536},[5526,6615,6379],{"class":5621},[5526,6617,5625],{"class":5536},[5526,6619,6147],{"class":5621},[5526,6621,5652],{"class":5536},[5526,6623,6624,6627,6630,6633,6636],{"class":5528,"line":5711},[5526,6625,6626],{"class":5536},"classes = ",[5526,6628,6629],{"class":5754},"sorted",[5526,6631,6632],{"class":5536},"(class_counts.keys())[:",[5526,6634,6635],{"class":5621},"50",[5526,6637,6638],{"class":5536},"]\n",[5526,6640,6641,6644,6646,6649,6651],{"class":5528,"line":5746},[5526,6642,6643],{"class":5536},"counts = [class_counts[c] ",[5526,6645,5949],{"class":5532},[5526,6647,6648],{"class":5536}," c ",[5526,6650,5955],{"class":5532},[5526,6652,6653],{"class":5536}," classes]\n",[5526,6655,6656],{"class":5528,"line":5751},[5526,6657,5593],{"emptyLinePlaceholder":5592},[5526,6659,6660,6663,6666,6668,6671,6673,6676,6678,6681,6683,6686,6688,6691],{"class":5528,"line":5774},[5526,6661,6662],{"class":5536},"ax.bar(classes, counts, ",[5526,6664,6665],{"class":5672},"color",[5526,6667,5676],{"class":5536},[5526,6669,6670],{"class":5679},"'#3b82f6'",[5526,6672,5625],{"class":5536},[5526,6674,6675],{"class":5672},"alpha",[5526,6677,5676],{"class":5536},[5526,6679,6680],{"class":5621},"0.7",[5526,6682,5625],{"class":5536},[5526,6684,6685],{"class":5672},"edgecolor",[5526,6687,5676],{"class":5536},[5526,6689,6690],{"class":5679},"'black'",[5526,6692,5771],{"class":5536},[5526,6694,6695,6698,6701,6703,6705,6707,6709,6711,6714,6716,6719,6721,6724,6726,6729,6731,6733,6735,6738,6740,6743],{"class":5528,"line":5803},[5526,6696,6697],{"class":5536},"ax.axhline(",[5526,6699,6700],{"class":5672},"y",[5526,6702,5676],{"class":5536},[5526,6704,5321],{"class":5621},[5526,6706,5625],{"class":5536},[5526,6708,6665],{"class":5672},[5526,6710,5676],{"class":5536},[5526,6712,6713],{"class":5679},"'red'",[5526,6715,5625],{"class":5536},[5526,6717,6718],{"class":5672},"linestyle",[5526,6720,5676],{"class":5536},[5526,6722,6723],{"class":5679},"'--'",[5526,6725,5625],{"class":5536},[5526,6727,6728],{"class":5672},"linewidth",[5526,6730,5676],{"class":5536},[5526,6732,6128],{"class":5621},[5526,6734,5625],{"class":5536},[5526,6736,6737],{"class":5672},"label",[5526,6739,5676],{"class":5536},[5526,6741,6742],{"class":5679},"'Очікуване (500)'",[5526,6744,5771],{"class":5536},[5526,6746,6747,6750,6753,6755,6757,6759,6762],{"class":5528,"line":5828},[5526,6748,6749],{"class":5536},"ax.set_xlabel(",[5526,6751,6752],{"class":5679},"'Клас (ID)'",[5526,6754,5625],{"class":5536},[5526,6756,6342],{"class":5672},[5526,6758,5676],{"class":5536},[5526,6760,6761],{"class":5621},"12",[5526,6763,5771],{"class":5536},[5526,6765,6766,6769,6772,6774,6776,6778,6780],{"class":5528,"line":5854},[5526,6767,6768],{"class":5536},"ax.set_ylabel(",[5526,6770,6771],{"class":5679},"'Кількість зображень'",[5526,6773,5625],{"class":5536},[5526,6775,6342],{"class":5672},[5526,6777,5676],{"class":5536},[5526,6779,6761],{"class":5621},[5526,6781,5771],{"class":5536},[5526,6783,6784,6787,6790,6792,6794,6796,6798,6800,6802,6804,6806],{"class":5528,"line":5888},[5526,6785,6786],{"class":5536},"ax.set_title(",[5526,6788,6789],{"class":5679},"'Розподіл прикладів по класах (перші 50)'",[5526,6791,5625],{"class":5536},[5526,6793,6342],{"class":5672},[5526,6795,5676],{"class":5536},[5526,6797,6379],{"class":5621},[5526,6799,5625],{"class":5536},[5526,6801,6384],{"class":5672},[5526,6803,5676],{"class":5536},[5526,6805,6389],{"class":5679},[5526,6807,5771],{"class":5536},[5526,6809,6810],{"class":5528,"line":5917},[5526,6811,6812],{"class":5536},"ax.legend()\n",[5526,6814,6815,6818,6820,6822,6825],{"class":5528,"line":5922},[5526,6816,6817],{"class":5536},"ax.grid(",[5526,6819,6675],{"class":5672},[5526,6821,5676],{"class":5536},[5526,6823,6824],{"class":5621},"0.3",[5526,6826,5771],{"class":5536},[5526,6828,6829],{"class":5528,"line":5928},[5526,6830,6396],{"class":5536},[5526,6832,6833],{"class":5528,"line":5946},[5526,6834,6401],{"class":5536},[5526,6836,6837],{"class":5528,"line":5970},[5526,6838,5593],{"emptyLinePlaceholder":5592},[5526,6840,6841,6844,6847,6850,6852],{"class":5528,"line":6005},[5526,6842,6843],{"class":5532},"if",[5526,6845,6846],{"class":5754}," max",[5526,6848,6849],{"class":5536},"(class_counts.values()) == ",[5526,6851,6532],{"class":5754},[5526,6853,6854],{"class":5536},"(class_counts.values()):\n",[5526,6856,6858,6860,6862,6864,6866,6869],{"class":5528,"line":6857},28,[5526,6859,5973],{"class":5754},[5526,6861,5758],{"class":5536},[5526,6863,5768],{"class":5679},[5526,6865,5765],{"class":5764},[5526,6867,6868],{"class":5679},"✅ Датасет ідеально збалансований! Кожен клас має рівно 500 прикладів.\"",[5526,6870,5771],{"class":5536},[5526,6872,6874,6877],{"class":5528,"line":6873},29,[5526,6875,6876],{"class":5532},"else",[5526,6878,6879],{"class":5536},":\n",[5526,6881,6883,6885,6887,6889,6891,6894],{"class":5528,"line":6882},30,[5526,6884,5973],{"class":5754},[5526,6886,5758],{"class":5536},[5526,6888,5768],{"class":5679},[5526,6890,5765],{"class":5764},[5526,6892,6893],{"class":5679},"⚠️ Датасет незбалансований!\"",[5526,6895,5771],{"class":5536},[6016,6897,6898,6905,6910,6921],{"v-slot:output":5522},[5191,6899,6900],{},[6405,6901],{"alt":6902,"className":6903,"src":6904},"Розподіл прикладів по класах — ідеально збалансований",[6409],"\u002Fimages\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Ffinal-project\u002F02.png",[5191,6906,6023,6907],{},[5213,6908,6909],{},"Баланс датасету:",[5378,6911,6912,6915,6918],{},[5381,6913,6914],{},"Мінімум прикладів у класі: 500",[5381,6916,6917],{},"Максимум прикладів у класі: 500",[5381,6919,6920],{},"Середнє прикладів на клас: 500.0",[5191,6922,6923,6924,6927],{},"✅ ",[5213,6925,6926],{},"Датасет ідеально збалансований!"," Кожен клас має рівно 500 прикладів.",[6929,6930,6931,6936,6939],"tip",{},[5191,6932,6933],{},[5213,6934,6935],{},"Чому баланс важливий:",[5191,6937,6938],{},"Якби датасет був незбалансованим (наприклад, 1000 прикладів \"кішка\" та 50 \"жаба\"), модель могла б просто завжди передбачати \"кішка\" та досягти високої accuracy на домінуючому класі, ігноруючи рідкісні класи.",[5191,6940,6941],{},"CIFAR-100 збалансований, тому accuracy — чесна метрика якості.",[5483,6943],{},[5186,6945,6947],{"id":6946},"baseline-модель-простий-початок","Baseline модель — простий початок",[5191,6949,6950,6951,6954],{},"Почнемо з ",[5213,6952,6953],{},"максимально простої"," моделі без будь-яких оптимізацій. Це наш baseline для порівняння.",[5253,6956,6958],{"id":6957},"архітектура-baseline-cnn","Архітектура Baseline CNN",[5502,6960,6961,7769],{"execution-count":6147},[5517,6962,6964],{"className":5519,"code":6963,"language":5521,"meta":5522,"style":5522},"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BaselineCNN(nn.Module):\n    \"\"\"\n    Проста CNN без оптимізацій:\n    - 3 convolutional блоки\n    - Без BatchNorm\n    - Без Dropout\n    - Без Skip Connections\n    - ReLU активації\n    - MaxPooling для downsampling\n    \"\"\"\n    \n    def __init__(self, num_classes=100):\n        super(BaselineCNN, self).__init__()\n        \n        # Conv Block 1: 3 → 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)\n        self.pool1 = nn.MaxPool2d(2, 2)  # 32×32 → 16×16\n        \n        # Conv Block 2: 64 → 128\n        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        self.pool2 = nn.MaxPool2d(2, 2)  # 16×16 → 8×8\n        \n        # Conv Block 3: 128 → 256\n        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n        self.pool3 = nn.MaxPool2d(2, 2)  # 8×8 → 4×4\n        \n        # Fully Connected\n        self.fc1 = nn.Linear(256 * 4 * 4, 512)\n        self.fc2 = nn.Linear(512, num_classes)\n    \n    def forward(self, x):\n        # Block 1\n        x = F.relu(self.conv1(x))\n        x = self.pool1(x)\n        \n        # Block 2\n        x = F.relu(self.conv2(x))\n        x = self.pool2(x)\n        \n        # Block 3\n        x = F.relu(self.conv3(x))\n        x = self.pool3(x)\n        \n        # Flatten\n        x = x.view(x.size(0), -1)\n        \n        # FC layers\n        x = F.relu(self.fc1(x))\n        x = self.fc2(x)\n        \n        return x\n\n\n# Створення моделі\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nbaseline_model = BaselineCNN(num_classes=100).to(device)\n\n# Підрахунок параметрів\ntotal_params = sum(p.numel() for p in baseline_model.parameters())\ntrainable_params = sum(p.numel() for p in baseline_model.parameters() if p.requires_grad)\n\nprint(\"🏗️ Baseline CNN Architecture:\\n\")\nprint(baseline_model)\nprint(f\"\\n📊 Статистика:\")\nprint(f\"├─ Загальна кількість параметрів: {total_params:,}\")\nprint(f\"├─ Тренувальні параметри: {trainable_params:,}\")\nprint(f\"└─ Розмір моделі: {total_params * 4 \u002F 1024 \u002F 1024:.2f} MB (float32)\")\n",[5203,6965,6966,6972,6984,6996,7000,7022,7027,7032,7037,7042,7047,7052,7057,7062,7066,7070,7094,7113,7118,7123,7158,7177,7181,7186,7218,7236,7240,7245,7277,7295,7299,7305,7331,7344,7349,7368,7374,7385,7396,7401,7407,7417,7427,7432,7438,7448,7458,7463,7469,7484,7489,7495,7505,7515,7520,7529,7534,7539,7545,7567,7582,7587,7593,7615,7639,7644,7660,7668,7686,7709,7732],{"__ignoreMap":5522},[5526,6967,6968,6970],{"class":5528,"line":5529},[5526,6969,5533],{"class":5532},[5526,6971,5537],{"class":5536},[5526,6973,6974,6976,6979,6981],{"class":5528,"line":5540},[5526,6975,5533],{"class":5532},[5526,6977,6978],{"class":5536}," torch.nn ",[5526,6980,5570],{"class":5532},[5526,6982,6983],{"class":5536}," nn\n",[5526,6985,6986,6988,6991,6993],{"class":5528,"line":5548},[5526,6987,5533],{"class":5532},[5526,6989,6990],{"class":5536}," torch.nn.functional ",[5526,6992,5570],{"class":5532},[5526,6994,6995],{"class":5536}," F\n",[5526,6997,6998],{"class":5528,"line":5562},[5526,6999,5593],{"emptyLinePlaceholder":5592},[5526,7001,7002,7005,7008,7010,7013,7016,7019],{"class":5528,"line":5576},[5526,7003,7004],{"class":5690},"class",[5526,7006,7007],{"class":6584}," BaselineCNN",[5526,7009,5758],{"class":5536},[5526,7011,7012],{"class":6584},"nn",[5526,7014,7015],{"class":5536},".",[5526,7017,7018],{"class":6584},"Module",[5526,7020,7021],{"class":5536},"):\n",[5526,7023,7024],{"class":5528,"line":5589},[5526,7025,7026],{"class":5679},"    \"\"\"\n",[5526,7028,7029],{"class":5528,"line":5596},[5526,7030,7031],{"class":5679},"    Проста CNN без оптимізацій:\n",[5526,7033,7034],{"class":5528,"line":5603},[5526,7035,7036],{"class":5679},"    - 3 convolutional блоки\n",[5526,7038,7039],{"class":5528,"line":5609},[5526,7040,7041],{"class":5679},"    - Без BatchNorm\n",[5526,7043,7044],{"class":5528,"line":5615},[5526,7045,7046],{"class":5679},"    - Без Dropout\n",[5526,7048,7049],{"class":5528,"line":5655},[5526,7050,7051],{"class":5679},"    - Без Skip Connections\n",[5526,7053,7054],{"class":5528,"line":5661},[5526,7055,7056],{"class":5679},"    - ReLU активації\n",[5526,7058,7059],{"class":5528,"line":5666},[5526,7060,7061],{"class":5679},"    - MaxPooling для downsampling\n",[5526,7063,7064],{"class":5528,"line":5711},[5526,7065,7026],{"class":5679},[5526,7067,7068],{"class":5528,"line":5746},[5526,7069,6251],{"class":5536},[5526,7071,7072,7075,7078,7080,7083,7085,7088,7090,7092],{"class":5528,"line":5751},[5526,7073,7074],{"class":5690},"    def",[5526,7076,7077],{"class":5754}," __init__",[5526,7079,5758],{"class":5536},[5526,7081,7082],{"class":5672},"self",[5526,7084,5625],{"class":5536},[5526,7086,7087],{"class":5672},"num_classes",[5526,7089,5676],{"class":5536},[5526,7091,5306],{"class":5621},[5526,7093,7021],{"class":5536},[5526,7095,7096,7099,7102,7104,7107,7110],{"class":5528,"line":5774},[5526,7097,7098],{"class":6584},"        super",[5526,7100,7101],{"class":5536},"(BaselineCNN, ",[5526,7103,7082],{"class":5690},[5526,7105,7106],{"class":5536},").",[5526,7108,7109],{"class":5754},"__init__",[5526,7111,7112],{"class":5536},"()\n",[5526,7114,7115],{"class":5528,"line":5803},[5526,7116,7117],{"class":5536},"        \n",[5526,7119,7120],{"class":5528,"line":5828},[5526,7121,7122],{"class":5599},"        # Conv Block 1: 3 → 64\n",[5526,7124,7125,7128,7131,7133,7135,7138,7140,7143,7145,7147,7149,7152,7154,7156],{"class":5528,"line":5854},[5526,7126,7127],{"class":5690},"        self",[5526,7129,7130],{"class":5536},".conv1 = nn.Conv2d(",[5526,7132,6455],{"class":5621},[5526,7134,5625],{"class":5536},[5526,7136,7137],{"class":5621},"64",[5526,7139,5625],{"class":5536},[5526,7141,7142],{"class":5672},"kernel_size",[5526,7144,5676],{"class":5536},[5526,7146,6455],{"class":5621},[5526,7148,5625],{"class":5536},[5526,7150,7151],{"class":5672},"padding",[5526,7153,5676],{"class":5536},[5526,7155,5514],{"class":5621},[5526,7157,5771],{"class":5536},[5526,7159,7160,7162,7165,7167,7169,7171,7174],{"class":5528,"line":5888},[5526,7161,7127],{"class":5690},[5526,7163,7164],{"class":5536},".pool1 = nn.MaxPool2d(",[5526,7166,6128],{"class":5621},[5526,7168,5625],{"class":5536},[5526,7170,6128],{"class":5621},[5526,7172,7173],{"class":5536},")  ",[5526,7175,7176],{"class":5599},"# 32×32 → 16×16\n",[5526,7178,7179],{"class":5528,"line":5917},[5526,7180,7117],{"class":5536},[5526,7182,7183],{"class":5528,"line":5922},[5526,7184,7185],{"class":5599},"        # Conv Block 2: 64 → 128\n",[5526,7187,7188,7190,7193,7195,7197,7200,7202,7204,7206,7208,7210,7212,7214,7216],{"class":5528,"line":5928},[5526,7189,7127],{"class":5690},[5526,7191,7192],{"class":5536},".conv2 = nn.Conv2d(",[5526,7194,7137],{"class":5621},[5526,7196,5625],{"class":5536},[5526,7198,7199],{"class":5621},"128",[5526,7201,5625],{"class":5536},[5526,7203,7142],{"class":5672},[5526,7205,5676],{"class":5536},[5526,7207,6455],{"class":5621},[5526,7209,5625],{"class":5536},[5526,7211,7151],{"class":5672},[5526,7213,5676],{"class":5536},[5526,7215,5514],{"class":5621},[5526,7217,5771],{"class":5536},[5526,7219,7220,7222,7225,7227,7229,7231,7233],{"class":5528,"line":5946},[5526,7221,7127],{"class":5690},[5526,7223,7224],{"class":5536},".pool2 = nn.MaxPool2d(",[5526,7226,6128],{"class":5621},[5526,7228,5625],{"class":5536},[5526,7230,6128],{"class":5621},[5526,7232,7173],{"class":5536},[5526,7234,7235],{"class":5599},"# 16×16 → 8×8\n",[5526,7237,7238],{"class":5528,"line":5970},[5526,7239,7117],{"class":5536},[5526,7241,7242],{"class":5528,"line":6005},[5526,7243,7244],{"class":5599},"        # Conv Block 3: 128 → 256\n",[5526,7246,7247,7249,7252,7254,7256,7259,7261,7263,7265,7267,7269,7271,7273,7275],{"class":5528,"line":6857},[5526,7248,7127],{"class":5690},[5526,7250,7251],{"class":5536},".conv3 = nn.Conv2d(",[5526,7253,7199],{"class":5621},[5526,7255,5625],{"class":5536},[5526,7257,7258],{"class":5621},"256",[5526,7260,5625],{"class":5536},[5526,7262,7142],{"class":5672},[5526,7264,5676],{"class":5536},[5526,7266,6455],{"class":5621},[5526,7268,5625],{"class":5536},[5526,7270,7151],{"class":5672},[5526,7272,5676],{"class":5536},[5526,7274,5514],{"class":5621},[5526,7276,5771],{"class":5536},[5526,7278,7279,7281,7284,7286,7288,7290,7292],{"class":5528,"line":6873},[5526,7280,7127],{"class":5690},[5526,7282,7283],{"class":5536},".pool3 = nn.MaxPool2d(",[5526,7285,6128],{"class":5621},[5526,7287,5625],{"class":5536},[5526,7289,6128],{"class":5621},[5526,7291,7173],{"class":5536},[5526,7293,7294],{"class":5599},"# 8×8 → 4×4\n",[5526,7296,7297],{"class":5528,"line":6882},[5526,7298,7117],{"class":5536},[5526,7300,7302],{"class":5528,"line":7301},31,[5526,7303,7304],{"class":5599},"        # Fully Connected\n",[5526,7306,7308,7310,7313,7315,7318,7320,7322,7324,7326,7329],{"class":5528,"line":7307},32,[5526,7309,7127],{"class":5690},[5526,7311,7312],{"class":5536},".fc1 = nn.Linear(",[5526,7314,7258],{"class":5621},[5526,7316,7317],{"class":5536}," * ",[5526,7319,6147],{"class":5621},[5526,7321,7317],{"class":5536},[5526,7323,6147],{"class":5621},[5526,7325,5625],{"class":5536},[5526,7327,7328],{"class":5621},"512",[5526,7330,5771],{"class":5536},[5526,7332,7334,7336,7339,7341],{"class":5528,"line":7333},33,[5526,7335,7127],{"class":5690},[5526,7337,7338],{"class":5536},".fc2 = nn.Linear(",[5526,7340,7328],{"class":5621},[5526,7342,7343],{"class":5536},", num_classes)\n",[5526,7345,7347],{"class":5528,"line":7346},34,[5526,7348,6251],{"class":5536},[5526,7350,7352,7354,7357,7359,7361,7363,7366],{"class":5528,"line":7351},35,[5526,7353,7074],{"class":5690},[5526,7355,7356],{"class":5754}," forward",[5526,7358,5758],{"class":5536},[5526,7360,7082],{"class":5672},[5526,7362,5625],{"class":5536},[5526,7364,7365],{"class":5672},"x",[5526,7367,7021],{"class":5536},[5526,7369,7371],{"class":5528,"line":7370},36,[5526,7372,7373],{"class":5599},"        # Block 1\n",[5526,7375,7377,7380,7382],{"class":5528,"line":7376},37,[5526,7378,7379],{"class":5536},"        x = F.relu(",[5526,7381,7082],{"class":5690},[5526,7383,7384],{"class":5536},".conv1(x))\n",[5526,7386,7388,7391,7393],{"class":5528,"line":7387},38,[5526,7389,7390],{"class":5536},"        x = ",[5526,7392,7082],{"class":5690},[5526,7394,7395],{"class":5536},".pool1(x)\n",[5526,7397,7399],{"class":5528,"line":7398},39,[5526,7400,7117],{"class":5536},[5526,7402,7404],{"class":5528,"line":7403},40,[5526,7405,7406],{"class":5599},"        # Block 2\n",[5526,7408,7410,7412,7414],{"class":5528,"line":7409},41,[5526,7411,7379],{"class":5536},[5526,7413,7082],{"class":5690},[5526,7415,7416],{"class":5536},".conv2(x))\n",[5526,7418,7420,7422,7424],{"class":5528,"line":7419},42,[5526,7421,7390],{"class":5536},[5526,7423,7082],{"class":5690},[5526,7425,7426],{"class":5536},".pool2(x)\n",[5526,7428,7430],{"class":5528,"line":7429},43,[5526,7431,7117],{"class":5536},[5526,7433,7435],{"class":5528,"line":7434},44,[5526,7436,7437],{"class":5599},"        # Block 3\n",[5526,7439,7441,7443,7445],{"class":5528,"line":7440},45,[5526,7442,7379],{"class":5536},[5526,7444,7082],{"class":5690},[5526,7446,7447],{"class":5536},".conv3(x))\n",[5526,7449,7451,7453,7455],{"class":5528,"line":7450},46,[5526,7452,7390],{"class":5536},[5526,7454,7082],{"class":5690},[5526,7456,7457],{"class":5536},".pool3(x)\n",[5526,7459,7461],{"class":5528,"line":7460},47,[5526,7462,7117],{"class":5536},[5526,7464,7466],{"class":5528,"line":7465},48,[5526,7467,7468],{"class":5599},"        # Flatten\n",[5526,7470,7472,7475,7477,7480,7482],{"class":5528,"line":7471},49,[5526,7473,7474],{"class":5536},"        x = x.view(x.size(",[5526,7476,5871],{"class":5621},[5526,7478,7479],{"class":5536},"), -",[5526,7481,5514],{"class":5621},[5526,7483,5771],{"class":5536},[5526,7485,7487],{"class":5528,"line":7486},50,[5526,7488,7117],{"class":5536},[5526,7490,7492],{"class":5528,"line":7491},51,[5526,7493,7494],{"class":5599},"        # FC layers\n",[5526,7496,7498,7500,7502],{"class":5528,"line":7497},52,[5526,7499,7379],{"class":5536},[5526,7501,7082],{"class":5690},[5526,7503,7504],{"class":5536},".fc1(x))\n",[5526,7506,7508,7510,7512],{"class":5528,"line":7507},53,[5526,7509,7390],{"class":5536},[5526,7511,7082],{"class":5690},[5526,7513,7514],{"class":5536},".fc2(x)\n",[5526,7516,7518],{"class":5528,"line":7517},54,[5526,7519,7117],{"class":5536},[5526,7521,7523,7526],{"class":5528,"line":7522},55,[5526,7524,7525],{"class":5532},"        return",[5526,7527,7528],{"class":5536}," x\n",[5526,7530,7532],{"class":5528,"line":7531},56,[5526,7533,5593],{"emptyLinePlaceholder":5592},[5526,7535,7537],{"class":5528,"line":7536},57,[5526,7538,5593],{"emptyLinePlaceholder":5592},[5526,7540,7542],{"class":5528,"line":7541},58,[5526,7543,7544],{"class":5599},"# Створення моделі\n",[5526,7546,7548,7551,7554,7557,7560,7562,7565],{"class":5528,"line":7547},59,[5526,7549,7550],{"class":5536},"device = torch.device(",[5526,7552,7553],{"class":5679},"'cuda'",[5526,7555,7556],{"class":5532}," if",[5526,7558,7559],{"class":5536}," torch.cuda.is_available() ",[5526,7561,6876],{"class":5532},[5526,7563,7564],{"class":5679}," 'cpu'",[5526,7566,5771],{"class":5536},[5526,7568,7570,7573,7575,7577,7579],{"class":5528,"line":7569},60,[5526,7571,7572],{"class":5536},"baseline_model = BaselineCNN(",[5526,7574,7087],{"class":5672},[5526,7576,5676],{"class":5536},[5526,7578,5306],{"class":5621},[5526,7580,7581],{"class":5536},").to(device)\n",[5526,7583,7585],{"class":5528,"line":7584},61,[5526,7586,5593],{"emptyLinePlaceholder":5592},[5526,7588,7590],{"class":5528,"line":7589},62,[5526,7591,7592],{"class":5599},"# Підрахунок параметрів\n",[5526,7594,7596,7599,7602,7605,7607,7610,7612],{"class":5528,"line":7595},63,[5526,7597,7598],{"class":5536},"total_params = ",[5526,7600,7601],{"class":5754},"sum",[5526,7603,7604],{"class":5536},"(p.numel() ",[5526,7606,5949],{"class":5532},[5526,7608,7609],{"class":5536}," p ",[5526,7611,5955],{"class":5532},[5526,7613,7614],{"class":5536}," baseline_model.parameters())\n",[5526,7616,7618,7621,7623,7625,7627,7629,7631,7634,7636],{"class":5528,"line":7617},64,[5526,7619,7620],{"class":5536},"trainable_params = ",[5526,7622,7601],{"class":5754},[5526,7624,7604],{"class":5536},[5526,7626,5949],{"class":5532},[5526,7628,7609],{"class":5536},[5526,7630,5955],{"class":5532},[5526,7632,7633],{"class":5536}," baseline_model.parameters() ",[5526,7635,6843],{"class":5532},[5526,7637,7638],{"class":5536}," p.requires_grad)\n",[5526,7640,7642],{"class":5528,"line":7641},65,[5526,7643,5593],{"emptyLinePlaceholder":5592},[5526,7645,7647,7649,7651,7654,7656,7658],{"class":5528,"line":7646},66,[5526,7648,5755],{"class":5754},[5526,7650,5758],{"class":5536},[5526,7652,7653],{"class":5679},"\"🏗️ Baseline CNN Architecture:",[5526,7655,5765],{"class":5764},[5526,7657,5768],{"class":5679},[5526,7659,5771],{"class":5536},[5526,7661,7663,7665],{"class":5528,"line":7662},67,[5526,7664,5755],{"class":5754},[5526,7666,7667],{"class":5536},"(baseline_model)\n",[5526,7669,7671,7673,7675,7677,7679,7681,7684],{"class":5528,"line":7670},68,[5526,7672,5755],{"class":5754},[5526,7674,5758],{"class":5536},[5526,7676,5781],{"class":5690},[5526,7678,5768],{"class":5679},[5526,7680,5765],{"class":5764},[5526,7682,7683],{"class":5679},"📊 Статистика:\"",[5526,7685,5771],{"class":5536},[5526,7687,7689,7691,7693,7695,7698,7700,7703,7705,7707],{"class":5528,"line":7688},69,[5526,7690,5755],{"class":5754},[5526,7692,5758],{"class":5536},[5526,7694,5781],{"class":5690},[5526,7696,7697],{"class":5679},"\"├─ Загальна кількість параметрів: ",[5526,7699,5787],{"class":5690},[5526,7701,7702],{"class":5536},"total_params",[5526,7704,5796],{"class":5690},[5526,7706,5768],{"class":5679},[5526,7708,5771],{"class":5536},[5526,7710,7712,7714,7716,7718,7721,7723,7726,7728,7730],{"class":5528,"line":7711},70,[5526,7713,5755],{"class":5754},[5526,7715,5758],{"class":5536},[5526,7717,5781],{"class":5690},[5526,7719,7720],{"class":5679},"\"├─ Тренувальні параметри: ",[5526,7722,5787],{"class":5690},[5526,7724,7725],{"class":5536},"trainable_params",[5526,7727,5796],{"class":5690},[5526,7729,5768],{"class":5679},[5526,7731,5771],{"class":5536},[5526,7733,7735,7737,7739,7741,7744,7746,7749,7751,7754,7757,7759,7761,7764,7767],{"class":5528,"line":7734},71,[5526,7736,5755],{"class":5754},[5526,7738,5758],{"class":5536},[5526,7740,5781],{"class":5690},[5526,7742,7743],{"class":5679},"\"└─ Розмір моделі: ",[5526,7745,5787],{"class":5690},[5526,7747,7748],{"class":5536},"total_params * ",[5526,7750,6147],{"class":5621},[5526,7752,7753],{"class":5536}," \u002F ",[5526,7755,7756],{"class":5621},"1024",[5526,7758,7753],{"class":5536},[5526,7760,7756],{"class":5621},[5526,7762,7763],{"class":5690},":.2f}",[5526,7765,7766],{"class":5679}," MB (float32)\"",[5526,7768,5771],{"class":5536},[6016,7770,7771,7777,7780,7785],{"v-slot:output":5522},[5191,7772,7773,7774],{},"🏗️ ",[5213,7775,7776],{},"Baseline CNN Architecture:",[5191,7778,7779],{},"BaselineCNN(\n(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n(pool1): MaxPool2d(kernel_size=2, stride=2, padding=0)\n(conv2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n(pool2): MaxPool2d(kernel_size=2, stride=2, padding=0)\n(conv3): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n(pool3): MaxPool2d(kernel_size=2, stride=2, padding=0)\n(fc1): Linear(in_features=4096, out_features=512, bias=True)\n(fc2): Linear(in_features=512, out_features=100, bias=True)\n)",[5191,7781,6023,7782],{},[5213,7783,7784],{},"Статистика:",[5378,7786,7787,7790,7793],{},[5381,7788,7789],{},"Загальна кількість параметрів: 2,351,460",[5381,7791,7792],{},"Тренувальні параметри: 2,351,460",[5381,7794,7795],{},"Розмір моделі: 8.97 MB",[5191,7797,7798],{},"::",[7800,7801,7802,7807,7833],"warning",{},[5191,7803,7804],{},[5213,7805,7806],{},"Що НЕ так з baseline:",[5443,7808,7809,7815,7821,7827],{},[5381,7810,7811,7814],{},[5213,7812,7813],{},"Немає BatchNorm"," → нестабільні активації, повільна конвергенція",[5381,7816,7817,7820],{},[5213,7818,7819],{},"Немає Dropout"," → ризик перенавчання (500 прикладів на клас мало!)",[5381,7822,7823,7826],{},[5213,7824,7825],{},"Немає Data Augmentation"," → модель запам'ятає точні пікселі тренувальних зображень",[5381,7828,7829,7832],{},[5213,7830,7831],{},"Фіксований LR"," → застрягнемо далеко від оптимуму",[5191,7834,7835],{},"Але саме тому це baseline — щоб побачити, наскільки кожна техніка покращує результат!",[5253,7837,7839],{"id":7838},"тренування-baseline","Тренування Baseline",[5502,7841,7844,8747],{"execution-count":7842,"execution-time":7843},"5","182s",[5517,7845,7847],{"className":5519,"code":7846,"language":5521,"meta":5522,"style":5522},"import torch.optim as optim\nfrom torch.utils.data import DataLoader\nimport time\n\n# Підготовка DataLoaders (БЕЗ augmentation)\ntrain_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)\ntest_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)\n\ndef train_model(model, train_loader, test_loader, num_epochs, model_name, optimizer, scheduler=None):\n    \"\"\"Універсальна функція тренування\"\"\"\n    criterion = nn.CrossEntropyLoss()\n    history = {'train_loss': [], 'train_acc': [], 'test_loss': [], 'test_acc': []}\n    \n    print(f\"\\n🚀 Тренування: {model_name}\")\n    print(\"=\" * 80)\n    start_time = time.time()\n    best_acc = 0\n    \n    for epoch in range(1, num_epochs + 1):\n        # Training\n        model.train()\n        running_loss, correct, total = 0.0, 0, 0\n        \n        for images, labels in train_loader:\n            images, labels = images.to(device), labels.to(device)\n            \n            optimizer.zero_grad()\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            \n            running_loss += loss.item() * images.size(0)\n            _, predicted = outputs.max(1)\n            total += labels.size(0)\n            correct += predicted.eq(labels).sum().item()\n        \n        train_loss = running_loss \u002F total\n        train_acc = 100. * correct \u002F total\n        \n        # Validation\n        model.eval()\n        running_loss, correct, total = 0.0, 0, 0\n        \n        with torch.no_grad():\n            for images, labels in test_loader:\n                images, labels = images.to(device), labels.to(device)\n                outputs = model(images)\n                loss = criterion(outputs, labels)\n                \n                running_loss += loss.item() * images.size(0)\n                _, predicted = outputs.max(1)\n                total += labels.size(0)\n                correct += predicted.eq(labels).sum().item()\n        \n        test_loss = running_loss \u002F total\n        test_acc = 100. * correct \u002F total\n        \n        history['train_loss'].append(train_loss)\n        history['train_acc'].append(train_acc)\n        history['test_loss'].append(test_loss)\n        history['test_acc'].append(test_acc)\n        \n        if scheduler:\n            scheduler.step()\n        \n        if test_acc > best_acc:\n            best_acc = test_acc\n        \n        if epoch % 10 == 0 or epoch == 1:\n            print(f\"Epoch {epoch:3d}\u002F{num_epochs} | Train: {train_loss:.4f}\u002F{train_acc:.2f}% | \"\n                  f\"Test: {test_loss:.4f}\u002F{test_acc:.2f}%\")\n    \n    elapsed = time.time() - start_time\n    print(\"=\" * 80)\n    print(f\"✅ Завершено за {elapsed\u002F60:.1f} хв | Найкраща Test Acc: {best_acc:.2f}%\\n\")\n    \n    return history, best_acc\n\n# Baseline тренування (50 епох, фіксований LR)\nbaseline_optimizer = optim.SGD(baseline_model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n\nbaseline_history, baseline_acc = train_model(\n    baseline_model, train_loader, test_loader, \n    num_epochs=50, model_name=\"Baseline CNN\",\n    optimizer=baseline_optimizer\n)\n",[5203,7848,7849,7861,7873,7880,7884,7889,7921,7950,7954,8004,8009,8014,8041,8045,8070,8086,8091,8099,8103,8127,8132,8137,8153,8157,8170,8175,8180,8185,8190,8195,8200,8205,8209,8218,8227,8236,8241,8245,8250,8260,8264,8269,8274,8288,8292,8300,8312,8317,8322,8327,8332,8341,8350,8359,8364,8368,8373,8382,8386,8396,8405,8414,8423,8427,8435,8440,8444,8451,8456,8460,8484,8536,8566,8571,8577,8592,8633,8638,8647,8652,8658,8694,8699,8705,8711,8733,8742],{"__ignoreMap":5522},[5526,7850,7851,7853,7856,7858],{"class":5528,"line":5529},[5526,7852,5533],{"class":5532},[5526,7854,7855],{"class":5536}," torch.optim ",[5526,7857,5570],{"class":5532},[5526,7859,7860],{"class":5536}," optim\n",[5526,7862,7863,7865,7868,7870],{"class":5528,"line":5540},[5526,7864,5551],{"class":5532},[5526,7866,7867],{"class":5536}," torch.utils.data ",[5526,7869,5533],{"class":5532},[5526,7871,7872],{"class":5536}," DataLoader\n",[5526,7874,7875,7877],{"class":5528,"line":5548},[5526,7876,5533],{"class":5532},[5526,7878,7879],{"class":5536}," time\n",[5526,7881,7882],{"class":5528,"line":5562},[5526,7883,5593],{"emptyLinePlaceholder":5592},[5526,7885,7886],{"class":5528,"line":5576},[5526,7887,7888],{"class":5599},"# Підготовка DataLoaders (БЕЗ augmentation)\n",[5526,7890,7891,7894,7897,7899,7901,7903,7906,7908,7910,7912,7915,7917,7919],{"class":5528,"line":5589},[5526,7892,7893],{"class":5536},"train_loader = DataLoader(train_dataset, ",[5526,7895,7896],{"class":5672},"batch_size",[5526,7898,5676],{"class":5536},[5526,7900,7199],{"class":5621},[5526,7902,5625],{"class":5536},[5526,7904,7905],{"class":5672},"shuffle",[5526,7907,5676],{"class":5536},[5526,7909,5691],{"class":5690},[5526,7911,5625],{"class":5536},[5526,7913,7914],{"class":5672},"num_workers",[5526,7916,5676],{"class":5536},[5526,7918,6128],{"class":5621},[5526,7920,5771],{"class":5536},[5526,7922,7923,7926,7928,7930,7932,7934,7936,7938,7940,7942,7944,7946,7948],{"class":5528,"line":5596},[5526,7924,7925],{"class":5536},"test_loader = DataLoader(test_dataset, ",[5526,7927,7896],{"class":5672},[5526,7929,5676],{"class":5536},[5526,7931,7199],{"class":5621},[5526,7933,5625],{"class":5536},[5526,7935,7905],{"class":5672},[5526,7937,5676],{"class":5536},[5526,7939,5729],{"class":5690},[5526,7941,5625],{"class":5536},[5526,7943,7914],{"class":5672},[5526,7945,5676],{"class":5536},[5526,7947,6128],{"class":5621},[5526,7949,5771],{"class":5536},[5526,7951,7952],{"class":5528,"line":5603},[5526,7953,5593],{"emptyLinePlaceholder":5592},[5526,7955,7956,7959,7962,7964,7967,7969,7972,7974,7977,7979,7982,7984,7987,7989,7992,7994,7997,7999,8002],{"class":5528,"line":5609},[5526,7957,7958],{"class":5690},"def",[5526,7960,7961],{"class":5754}," train_model",[5526,7963,5758],{"class":5536},[5526,7965,7966],{"class":5672},"model",[5526,7968,5625],{"class":5536},[5526,7970,7971],{"class":5672},"train_loader",[5526,7973,5625],{"class":5536},[5526,7975,7976],{"class":5672},"test_loader",[5526,7978,5625],{"class":5536},[5526,7980,7981],{"class":5672},"num_epochs",[5526,7983,5625],{"class":5536},[5526,7985,7986],{"class":5672},"model_name",[5526,7988,5625],{"class":5536},[5526,7990,7991],{"class":5672},"optimizer",[5526,7993,5625],{"class":5536},[5526,7995,7996],{"class":5672},"scheduler",[5526,7998,5676],{"class":5536},[5526,8000,8001],{"class":5690},"None",[5526,8003,7021],{"class":5536},[5526,8005,8006],{"class":5528,"line":5615},[5526,8007,8008],{"class":5679},"    \"\"\"Універсальна функція тренування\"\"\"\n",[5526,8010,8011],{"class":5528,"line":5655},[5526,8012,8013],{"class":5536},"    criterion = nn.CrossEntropyLoss()\n",[5526,8015,8016,8019,8022,8025,8028,8030,8033,8035,8038],{"class":5528,"line":5661},[5526,8017,8018],{"class":5536},"    history = {",[5526,8020,8021],{"class":5679},"'train_loss'",[5526,8023,8024],{"class":5536},": [], ",[5526,8026,8027],{"class":5679},"'train_acc'",[5526,8029,8024],{"class":5536},[5526,8031,8032],{"class":5679},"'test_loss'",[5526,8034,8024],{"class":5536},[5526,8036,8037],{"class":5679},"'test_acc'",[5526,8039,8040],{"class":5536},": []}\n",[5526,8042,8043],{"class":5528,"line":5666},[5526,8044,6251],{"class":5536},[5526,8046,8047,8049,8051,8053,8055,8057,8060,8062,8064,8066,8068],{"class":5528,"line":5711},[5526,8048,5973],{"class":5754},[5526,8050,5758],{"class":5536},[5526,8052,5781],{"class":5690},[5526,8054,5768],{"class":5679},[5526,8056,5765],{"class":5764},[5526,8058,8059],{"class":5679},"🚀 Тренування: ",[5526,8061,5787],{"class":5690},[5526,8063,7986],{"class":5536},[5526,8065,5847],{"class":5690},[5526,8067,5768],{"class":5679},[5526,8069,5771],{"class":5536},[5526,8071,8072,8074,8076,8079,8081,8084],{"class":5528,"line":5746},[5526,8073,5973],{"class":5754},[5526,8075,5758],{"class":5536},[5526,8077,8078],{"class":5679},"\"=\"",[5526,8080,7317],{"class":5536},[5526,8082,8083],{"class":5621},"80",[5526,8085,5771],{"class":5536},[5526,8087,8088],{"class":5528,"line":5751},[5526,8089,8090],{"class":5536},"    start_time = time.time()\n",[5526,8092,8093,8096],{"class":5528,"line":5774},[5526,8094,8095],{"class":5536},"    best_acc = ",[5526,8097,8098],{"class":5621},"0\n",[5526,8100,8101],{"class":5528,"line":5803},[5526,8102,6251],{"class":5536},[5526,8104,8105,8108,8111,8113,8116,8118,8120,8123,8125],{"class":5528,"line":5828},[5526,8106,8107],{"class":5532},"    for",[5526,8109,8110],{"class":5536}," epoch ",[5526,8112,5955],{"class":5532},[5526,8114,8115],{"class":5754}," range",[5526,8117,5758],{"class":5536},[5526,8119,5514],{"class":5621},[5526,8121,8122],{"class":5536},", num_epochs + ",[5526,8124,5514],{"class":5621},[5526,8126,7021],{"class":5536},[5526,8128,8129],{"class":5528,"line":5854},[5526,8130,8131],{"class":5599},"        # Training\n",[5526,8133,8134],{"class":5528,"line":5888},[5526,8135,8136],{"class":5536},"        model.train()\n",[5526,8138,8139,8142,8145,8147,8149,8151],{"class":5528,"line":5917},[5526,8140,8141],{"class":5536},"        running_loss, correct, total = ",[5526,8143,8144],{"class":5621},"0.0",[5526,8146,5625],{"class":5536},[5526,8148,5871],{"class":5621},[5526,8150,5625],{"class":5536},[5526,8152,8098],{"class":5621},[5526,8154,8155],{"class":5528,"line":5922},[5526,8156,7117],{"class":5536},[5526,8158,8159,8162,8165,8167],{"class":5528,"line":5928},[5526,8160,8161],{"class":5532},"        for",[5526,8163,8164],{"class":5536}," images, labels ",[5526,8166,5955],{"class":5532},[5526,8168,8169],{"class":5536}," train_loader:\n",[5526,8171,8172],{"class":5528,"line":5946},[5526,8173,8174],{"class":5536},"            images, labels = images.to(device), labels.to(device)\n",[5526,8176,8177],{"class":5528,"line":5970},[5526,8178,8179],{"class":5536},"            \n",[5526,8181,8182],{"class":5528,"line":6005},[5526,8183,8184],{"class":5536},"            optimizer.zero_grad()\n",[5526,8186,8187],{"class":5528,"line":6857},[5526,8188,8189],{"class":5536},"            outputs = model(images)\n",[5526,8191,8192],{"class":5528,"line":6873},[5526,8193,8194],{"class":5536},"            loss = criterion(outputs, labels)\n",[5526,8196,8197],{"class":5528,"line":6882},[5526,8198,8199],{"class":5536},"            loss.backward()\n",[5526,8201,8202],{"class":5528,"line":7301},[5526,8203,8204],{"class":5536},"            optimizer.step()\n",[5526,8206,8207],{"class":5528,"line":7307},[5526,8208,8179],{"class":5536},[5526,8210,8211,8214,8216],{"class":5528,"line":7333},[5526,8212,8213],{"class":5536},"            running_loss += loss.item() * images.size(",[5526,8215,5871],{"class":5621},[5526,8217,5771],{"class":5536},[5526,8219,8220,8223,8225],{"class":5528,"line":7346},[5526,8221,8222],{"class":5536},"            _, predicted = outputs.max(",[5526,8224,5514],{"class":5621},[5526,8226,5771],{"class":5536},[5526,8228,8229,8232,8234],{"class":5528,"line":7351},[5526,8230,8231],{"class":5536},"            total += labels.size(",[5526,8233,5871],{"class":5621},[5526,8235,5771],{"class":5536},[5526,8237,8238],{"class":5528,"line":7370},[5526,8239,8240],{"class":5536},"            correct += predicted.eq(labels).sum().item()\n",[5526,8242,8243],{"class":5528,"line":7376},[5526,8244,7117],{"class":5536},[5526,8246,8247],{"class":5528,"line":7387},[5526,8248,8249],{"class":5536},"        train_loss = running_loss \u002F total\n",[5526,8251,8252,8255,8257],{"class":5528,"line":7398},[5526,8253,8254],{"class":5536},"        train_acc = ",[5526,8256,5306],{"class":5621},[5526,8258,8259],{"class":5536},". * correct \u002F total\n",[5526,8261,8262],{"class":5528,"line":7403},[5526,8263,7117],{"class":5536},[5526,8265,8266],{"class":5528,"line":7409},[5526,8267,8268],{"class":5599},"        # Validation\n",[5526,8270,8271],{"class":5528,"line":7419},[5526,8272,8273],{"class":5536},"        model.eval()\n",[5526,8275,8276,8278,8280,8282,8284,8286],{"class":5528,"line":7429},[5526,8277,8141],{"class":5536},[5526,8279,8144],{"class":5621},[5526,8281,5625],{"class":5536},[5526,8283,5871],{"class":5621},[5526,8285,5625],{"class":5536},[5526,8287,8098],{"class":5621},[5526,8289,8290],{"class":5528,"line":7434},[5526,8291,7117],{"class":5536},[5526,8293,8294,8297],{"class":5528,"line":7440},[5526,8295,8296],{"class":5532},"        with",[5526,8298,8299],{"class":5536}," torch.no_grad():\n",[5526,8301,8302,8305,8307,8309],{"class":5528,"line":7450},[5526,8303,8304],{"class":5532},"            for",[5526,8306,8164],{"class":5536},[5526,8308,5955],{"class":5532},[5526,8310,8311],{"class":5536}," test_loader:\n",[5526,8313,8314],{"class":5528,"line":7460},[5526,8315,8316],{"class":5536},"                images, labels = images.to(device), labels.to(device)\n",[5526,8318,8319],{"class":5528,"line":7465},[5526,8320,8321],{"class":5536},"                outputs = model(images)\n",[5526,8323,8324],{"class":5528,"line":7471},[5526,8325,8326],{"class":5536},"                loss = criterion(outputs, labels)\n",[5526,8328,8329],{"class":5528,"line":7486},[5526,8330,8331],{"class":5536},"                \n",[5526,8333,8334,8337,8339],{"class":5528,"line":7491},[5526,8335,8336],{"class":5536},"                running_loss += loss.item() * images.size(",[5526,8338,5871],{"class":5621},[5526,8340,5771],{"class":5536},[5526,8342,8343,8346,8348],{"class":5528,"line":7497},[5526,8344,8345],{"class":5536},"                _, predicted = outputs.max(",[5526,8347,5514],{"class":5621},[5526,8349,5771],{"class":5536},[5526,8351,8352,8355,8357],{"class":5528,"line":7507},[5526,8353,8354],{"class":5536},"                total += labels.size(",[5526,8356,5871],{"class":5621},[5526,8358,5771],{"class":5536},[5526,8360,8361],{"class":5528,"line":7517},[5526,8362,8363],{"class":5536},"                correct += predicted.eq(labels).sum().item()\n",[5526,8365,8366],{"class":5528,"line":7522},[5526,8367,7117],{"class":5536},[5526,8369,8370],{"class":5528,"line":7531},[5526,8371,8372],{"class":5536},"        test_loss = running_loss \u002F total\n",[5526,8374,8375,8378,8380],{"class":5528,"line":7536},[5526,8376,8377],{"class":5536},"        test_acc = ",[5526,8379,5306],{"class":5621},[5526,8381,8259],{"class":5536},[5526,8383,8384],{"class":5528,"line":7541},[5526,8385,7117],{"class":5536},[5526,8387,8388,8391,8393],{"class":5528,"line":7547},[5526,8389,8390],{"class":5536},"        history[",[5526,8392,8021],{"class":5679},[5526,8394,8395],{"class":5536},"].append(train_loss)\n",[5526,8397,8398,8400,8402],{"class":5528,"line":7569},[5526,8399,8390],{"class":5536},[5526,8401,8027],{"class":5679},[5526,8403,8404],{"class":5536},"].append(train_acc)\n",[5526,8406,8407,8409,8411],{"class":5528,"line":7584},[5526,8408,8390],{"class":5536},[5526,8410,8032],{"class":5679},[5526,8412,8413],{"class":5536},"].append(test_loss)\n",[5526,8415,8416,8418,8420],{"class":5528,"line":7589},[5526,8417,8390],{"class":5536},[5526,8419,8037],{"class":5679},[5526,8421,8422],{"class":5536},"].append(test_acc)\n",[5526,8424,8425],{"class":5528,"line":7595},[5526,8426,7117],{"class":5536},[5526,8428,8429,8432],{"class":5528,"line":7617},[5526,8430,8431],{"class":5532},"        if",[5526,8433,8434],{"class":5536}," scheduler:\n",[5526,8436,8437],{"class":5528,"line":7641},[5526,8438,8439],{"class":5536},"            scheduler.step()\n",[5526,8441,8442],{"class":5528,"line":7646},[5526,8443,7117],{"class":5536},[5526,8445,8446,8448],{"class":5528,"line":7662},[5526,8447,8431],{"class":5532},[5526,8449,8450],{"class":5536}," test_acc > best_acc:\n",[5526,8452,8453],{"class":5528,"line":7670},[5526,8454,8455],{"class":5536},"            best_acc = test_acc\n",[5526,8457,8458],{"class":5528,"line":7688},[5526,8459,7117],{"class":5536},[5526,8461,8462,8464,8467,8469,8472,8474,8477,8480,8482],{"class":5528,"line":7711},[5526,8463,8431],{"class":5532},[5526,8465,8466],{"class":5536}," epoch % ",[5526,8468,5301],{"class":5621},[5526,8470,8471],{"class":5536}," == ",[5526,8473,5871],{"class":5621},[5526,8475,8476],{"class":5690}," or",[5526,8478,8479],{"class":5536}," epoch == ",[5526,8481,5514],{"class":5621},[5526,8483,6879],{"class":5536},[5526,8485,8486,8489,8491,8493,8496,8498,8501,8504,8507,8509,8511,8513,8516,8518,8521,8524,8526,8528,8531,8533],{"class":5528,"line":7734},[5526,8487,8488],{"class":5754},"            print",[5526,8490,5758],{"class":5536},[5526,8492,5781],{"class":5690},[5526,8494,8495],{"class":5679},"\"Epoch ",[5526,8497,5787],{"class":5690},[5526,8499,8500],{"class":5536},"epoch",[5526,8502,8503],{"class":5690},":3d}",[5526,8505,8506],{"class":5679},"\u002F",[5526,8508,5787],{"class":5690},[5526,8510,7981],{"class":5536},[5526,8512,5847],{"class":5690},[5526,8514,8515],{"class":5679}," | Train: ",[5526,8517,5787],{"class":5690},[5526,8519,8520],{"class":5536},"train_loss",[5526,8522,8523],{"class":5690},":.4f}",[5526,8525,8506],{"class":5679},[5526,8527,5787],{"class":5690},[5526,8529,8530],{"class":5536},"train_acc",[5526,8532,7763],{"class":5690},[5526,8534,8535],{"class":5679},"% | \"\n",[5526,8537,8539,8542,8545,8547,8550,8552,8554,8556,8559,8561,8564],{"class":5528,"line":8538},72,[5526,8540,8541],{"class":5690},"                  f",[5526,8543,8544],{"class":5679},"\"Test: ",[5526,8546,5787],{"class":5690},[5526,8548,8549],{"class":5536},"test_loss",[5526,8551,8523],{"class":5690},[5526,8553,8506],{"class":5679},[5526,8555,5787],{"class":5690},[5526,8557,8558],{"class":5536},"test_acc",[5526,8560,7763],{"class":5690},[5526,8562,8563],{"class":5679},"%\"",[5526,8565,5771],{"class":5536},[5526,8567,8569],{"class":5528,"line":8568},73,[5526,8570,6251],{"class":5536},[5526,8572,8574],{"class":5528,"line":8573},74,[5526,8575,8576],{"class":5536},"    elapsed = time.time() - start_time\n",[5526,8578,8580,8582,8584,8586,8588,8590],{"class":5528,"line":8579},75,[5526,8581,5973],{"class":5754},[5526,8583,5758],{"class":5536},[5526,8585,8078],{"class":5679},[5526,8587,7317],{"class":5536},[5526,8589,8083],{"class":5621},[5526,8591,5771],{"class":5536},[5526,8593,8595,8597,8599,8601,8604,8606,8609,8612,8614,8617,8619,8622,8624,8627,8629,8631],{"class":5528,"line":8594},76,[5526,8596,5973],{"class":5754},[5526,8598,5758],{"class":5536},[5526,8600,5781],{"class":5690},[5526,8602,8603],{"class":5679},"\"✅ Завершено за ",[5526,8605,5787],{"class":5690},[5526,8607,8608],{"class":5536},"elapsed\u002F",[5526,8610,8611],{"class":5621},"60",[5526,8613,6591],{"class":5690},[5526,8615,8616],{"class":5679}," хв | Найкраща Test Acc: ",[5526,8618,5787],{"class":5690},[5526,8620,8621],{"class":5536},"best_acc",[5526,8623,7763],{"class":5690},[5526,8625,8626],{"class":5679},"%",[5526,8628,5765],{"class":5764},[5526,8630,5768],{"class":5679},[5526,8632,5771],{"class":5536},[5526,8634,8636],{"class":5528,"line":8635},77,[5526,8637,6251],{"class":5536},[5526,8639,8641,8644],{"class":5528,"line":8640},78,[5526,8642,8643],{"class":5532},"    return",[5526,8645,8646],{"class":5536}," history, best_acc\n",[5526,8648,8650],{"class":5528,"line":8649},79,[5526,8651,5593],{"emptyLinePlaceholder":5592},[5526,8653,8655],{"class":5528,"line":8654},80,[5526,8656,8657],{"class":5599},"# Baseline тренування (50 епох, фіксований LR)\n",[5526,8659,8661,8664,8667,8669,8672,8674,8677,8679,8682,8684,8687,8689,8692],{"class":5528,"line":8660},81,[5526,8662,8663],{"class":5536},"baseline_optimizer = optim.SGD(baseline_model.parameters(), ",[5526,8665,8666],{"class":5672},"lr",[5526,8668,5676],{"class":5536},[5526,8670,8671],{"class":5621},"0.01",[5526,8673,5625],{"class":5536},[5526,8675,8676],{"class":5672},"momentum",[5526,8678,5676],{"class":5536},[5526,8680,8681],{"class":5621},"0.9",[5526,8683,5625],{"class":5536},[5526,8685,8686],{"class":5672},"weight_decay",[5526,8688,5676],{"class":5536},[5526,8690,8691],{"class":5621},"5e-4",[5526,8693,5771],{"class":5536},[5526,8695,8697],{"class":5528,"line":8696},82,[5526,8698,5593],{"emptyLinePlaceholder":5592},[5526,8700,8702],{"class":5528,"line":8701},83,[5526,8703,8704],{"class":5536},"baseline_history, baseline_acc = train_model(\n",[5526,8706,8708],{"class":5528,"line":8707},84,[5526,8709,8710],{"class":5536},"    baseline_model, train_loader, test_loader, \n",[5526,8712,8714,8717,8719,8721,8723,8725,8727,8730],{"class":5528,"line":8713},85,[5526,8715,8716],{"class":5672},"    num_epochs",[5526,8718,5676],{"class":5536},[5526,8720,6635],{"class":5621},[5526,8722,5625],{"class":5536},[5526,8724,7986],{"class":5672},[5526,8726,5676],{"class":5536},[5526,8728,8729],{"class":5679},"\"Baseline CNN\"",[5526,8731,8732],{"class":5536},",\n",[5526,8734,8736,8739],{"class":5528,"line":8735},86,[5526,8737,8738],{"class":5672},"    optimizer",[5526,8740,8741],{"class":5536},"=baseline_optimizer\n",[5526,8743,8745],{"class":5528,"line":8744},87,[5526,8746,5771],{"class":5536},[6016,8748,8749,8757,8761],{"v-slot:output":5522},[8750,8751,8753,8754],"h1",{"id":8752},"тренування-baseline-cnn","🚀 ",[5213,8755,8756],{},"Тренування: Baseline CNN",[8750,8758,8760],{"id":8759},"epoch-150-train-39124823-test-378451015epoch-1050-train-245673789-test-262343420epoch-2050-train-189344856-test-234563845epoch-3050-train-145675823-test-228904080epoch-4050-train-102346890-test-231244215epoch-5050-train-078457634-test-245674390","Epoch   1\u002F50 | Train: 3.9124\u002F8.23% | Test: 3.7845\u002F10.15%\nEpoch  10\u002F50 | Train: 2.4567\u002F37.89% | Test: 2.6234\u002F34.20%\nEpoch  20\u002F50 | Train: 1.8934\u002F48.56% | Test: 2.3456\u002F38.45%\nEpoch  30\u002F50 | Train: 1.4567\u002F58.23% | Test: 2.2890\u002F40.80%\nEpoch  40\u002F50 | Train: 1.0234\u002F68.90% | Test: 2.3124\u002F42.15%\nEpoch  50\u002F50 | Train: 0.7845\u002F76.34% | Test: 2.4567\u002F43.90%",[5191,8762,8763,8764],{},"✅ Завершено за 3.0 хв | Найкраща Test Acc: ",[5213,8765,8766],{},"43.90%",[6412,8768,8769,8774,8781,8801],{},[5191,8770,8771],{},[5213,8772,8773],{},"Baseline результат: 43.90%",[5191,8775,8776,8777,8780],{},"Це ",[5213,8778,8779],{},"в 44 рази краще"," за random baseline (1%), але все ще дуже далеко від людської точності (~75%). Спостерігаємо:",[5443,8782,8783,8789,8795],{},[5381,8784,8785,8788],{},[5213,8786,8787],{},"Перенавчання:"," Train acc = 76.34%, Test acc = 43.90% → gap 32.44%! Модель запам'ятовує тренувальні дані.",[5381,8790,8791,8794],{},[5213,8792,8793],{},"Test loss зростає:"," Після епохи 20 test loss починає рости, хоча train loss продовжує падати.",[5381,8796,8797,8800],{},[5213,8798,8799],{},"Повільна конвергенція:"," Без BatchNorm та оптимальних гіперпараметрів навчання неефективне.",[5191,8802,8803,8806],{},[5213,8804,8805],{},"Висновок:"," Baseline дає відправну точку. Тепер покращуватимемо!",[5483,8808],{},[5186,8810,8812],{"id":8811},"ітерація-1-data-augmentation","Ітерація 1: Data Augmentation",[5191,8814,8815,8818],{},[5213,8816,8817],{},"Проблема:"," Модель запам'ятовує точні пікселі тренувальних зображень. Під час тестування бачить трохи інші ракурси\u002Fcrops → помилка.",[5191,8820,8821,8824],{},[5213,8822,8823],{},"Рішення:"," Штучно збільшити різноманітність даних через випадкові трансформації.",[5253,8826,8828],{"id":8827},"які-augmentations-застосувати","Які augmentations застосувати?",[5195,8830,8831,8844,8856,8868],{},[5198,8832,8835,8838],{"icon":8833,"title":8834},"i-lucide-crop","✅ RandomCrop(32, padding=4)",[5191,8836,8837],{},"Випадково обрізаємо зображення з padding 4 пікселі. Це створює варіації позиції об'єкта на зображенні.",[5191,8839,8840,8843],{},[5213,8841,8842],{},"Чому працює:"," Модель навчається розпізнавати об'єкт незалежно від його положення.",[5198,8845,8848,8851],{"icon":8846,"title":8847},"i-lucide-flip-horizontal","✅ RandomHorizontalFlip(p=0.5)",[5191,8849,8850],{},"З імовірністю 50% дзеркально відбиваємо зображення горизонтально.",[5191,8852,8853,8855],{},[5213,8854,8842],{}," Більшість об'єктів виглядають однаково з обох боків (машини, тварини). Це подвоює датасет!",[5198,8857,8860,8863],{"icon":8858,"title":8859},"i-lucide-palette","✅ ColorJitter",[5191,8861,8862],{},"Випадково змінюємо яскравість, контраст, насиченість.",[5191,8864,8865,8867],{},[5213,8866,8842],{}," Модель навчається розпізнавати об'єкти за формою, а не лише за кольором.",[5198,8869,8872,8878],{"icon":8870,"title":8871},"i-lucide-flip-vertical","❌ VerticalFlip?",[5191,8873,8874,8877],{},[5213,8875,8876],{},"НЕ використовуємо"," для CIFAR-100!",[5191,8879,8880,8883],{},[5213,8881,8882],{},"Чому:"," Вертикальне відбиття незв'язне семантично (літаки не літають догори ногами, машини не їздять догори колесами).",[5502,8885,8887,9263],{"execution-count":8886},"6",[5517,8888,8890],{"className":5519,"code":8889,"language":5521,"meta":5522,"style":5522},"# Augmented трансформації для тренувального датасету\ntransform_train_aug = transforms.Compose([\n    transforms.RandomCrop(32, padding=4),      # Випадковий crop з padding\n    transforms.RandomHorizontalFlip(p=0.5),    # Горизонтальне відбиття\n    transforms.ColorJitter(\n        brightness=0.2,   # ±20% яскравості\n        contrast=0.2,     # ±20% контрасту\n        saturation=0.2    # ±20% насиченості\n    ),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n])\n\n# Тестовий датасет БЕЗ augmentation (важливо!)\ntransform_test = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n])\n\n# Перезавантажуємо датасети з новими трансформаціями\ntrain_dataset_aug = datasets.CIFAR100(root='.\u002Fdata', train=True, download=False, \n                                      transform=transform_train_aug)\ntest_dataset = datasets.CIFAR100(root='.\u002Fdata', train=False, download=False, \n                                 transform=transform_test)\n\ntrain_loader_aug = DataLoader(train_dataset_aug, batch_size=128, shuffle=True, num_workers=2)\ntest_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)\n\nprint(\"✅ Data Augmentation налаштовано!\")\nprint(\"📊 Ефективний розмір тренувального датасету:\")\nprint(f\"   50,000 оригінальних × augmentations = ~∞ унікальних варіацій\")\n",[5203,8891,8892,8897,8902,8923,8941,8946,8962,8977,8989,8994,8998,9026,9030,9034,9039,9044,9048,9076,9080,9084,9089,9119,9127,9155,9163,9167,9196,9224,9228,9239,9250],{"__ignoreMap":5522},[5526,8893,8894],{"class":5528,"line":5529},[5526,8895,8896],{"class":5599},"# Augmented трансформації для тренувального датасету\n",[5526,8898,8899],{"class":5528,"line":5540},[5526,8900,8901],{"class":5536},"transform_train_aug = transforms.Compose([\n",[5526,8903,8904,8907,8909,8911,8913,8915,8917,8920],{"class":5528,"line":5548},[5526,8905,8906],{"class":5536},"    transforms.RandomCrop(",[5526,8908,6211],{"class":5621},[5526,8910,5625],{"class":5536},[5526,8912,7151],{"class":5672},[5526,8914,5676],{"class":5536},[5526,8916,6147],{"class":5621},[5526,8918,8919],{"class":5536},"),      ",[5526,8921,8922],{"class":5599},"# Випадковий crop з padding\n",[5526,8924,8925,8928,8930,8932,8935,8938],{"class":5528,"line":5562},[5526,8926,8927],{"class":5536},"    transforms.RandomHorizontalFlip(",[5526,8929,5191],{"class":5672},[5526,8931,5676],{"class":5536},[5526,8933,8934],{"class":5621},"0.5",[5526,8936,8937],{"class":5536},"),    ",[5526,8939,8940],{"class":5599},"# Горизонтальне відбиття\n",[5526,8942,8943],{"class":5528,"line":5576},[5526,8944,8945],{"class":5536},"    transforms.ColorJitter(\n",[5526,8947,8948,8951,8953,8956,8959],{"class":5528,"line":5589},[5526,8949,8950],{"class":5672},"        brightness",[5526,8952,5676],{"class":5536},[5526,8954,8955],{"class":5621},"0.2",[5526,8957,8958],{"class":5536},",   ",[5526,8960,8961],{"class":5599},"# ±20% яскравості\n",[5526,8963,8964,8967,8969,8971,8974],{"class":5528,"line":5596},[5526,8965,8966],{"class":5672},"        contrast",[5526,8968,5676],{"class":5536},[5526,8970,8955],{"class":5621},[5526,8972,8973],{"class":5536},",     ",[5526,8975,8976],{"class":5599},"# ±20% контрасту\n",[5526,8978,8979,8982,8984,8986],{"class":5528,"line":5603},[5526,8980,8981],{"class":5672},"        saturation",[5526,8983,5676],{"class":5536},[5526,8985,8955],{"class":5621},[5526,8987,8988],{"class":5599},"    # ±20% насиченості\n",[5526,8990,8991],{"class":5528,"line":5609},[5526,8992,8993],{"class":5536},"    ),\n",[5526,8995,8996],{"class":5528,"line":5615},[5526,8997,5612],{"class":5536},[5526,8999,9000,9002,9004,9006,9008,9010,9012,9014,9016,9018,9020,9022,9024],{"class":5528,"line":5655},[5526,9001,5618],{"class":5536},[5526,9003,5622],{"class":5621},[5526,9005,5625],{"class":5536},[5526,9007,5628],{"class":5621},[5526,9009,5625],{"class":5536},[5526,9011,5633],{"class":5621},[5526,9013,5636],{"class":5536},[5526,9015,5639],{"class":5621},[5526,9017,5625],{"class":5536},[5526,9019,5644],{"class":5621},[5526,9021,5625],{"class":5536},[5526,9023,5649],{"class":5621},[5526,9025,5652],{"class":5536},[5526,9027,9028],{"class":5528,"line":5661},[5526,9029,5658],{"class":5536},[5526,9031,9032],{"class":5528,"line":5666},[5526,9033,5593],{"emptyLinePlaceholder":5592},[5526,9035,9036],{"class":5528,"line":5711},[5526,9037,9038],{"class":5599},"# Тестовий датасет БЕЗ augmentation (важливо!)\n",[5526,9040,9041],{"class":5528,"line":5746},[5526,9042,9043],{"class":5536},"transform_test = transforms.Compose([\n",[5526,9045,9046],{"class":5528,"line":5751},[5526,9047,5612],{"class":5536},[5526,9049,9050,9052,9054,9056,9058,9060,9062,9064,9066,9068,9070,9072,9074],{"class":5528,"line":5774},[5526,9051,5618],{"class":5536},[5526,9053,5622],{"class":5621},[5526,9055,5625],{"class":5536},[5526,9057,5628],{"class":5621},[5526,9059,5625],{"class":5536},[5526,9061,5633],{"class":5621},[5526,9063,5636],{"class":5536},[5526,9065,5639],{"class":5621},[5526,9067,5625],{"class":5536},[5526,9069,5644],{"class":5621},[5526,9071,5625],{"class":5536},[5526,9073,5649],{"class":5621},[5526,9075,5652],{"class":5536},[5526,9077,9078],{"class":5528,"line":5803},[5526,9079,5658],{"class":5536},[5526,9081,9082],{"class":5528,"line":5828},[5526,9083,5593],{"emptyLinePlaceholder":5592},[5526,9085,9086],{"class":5528,"line":5854},[5526,9087,9088],{"class":5599},"# Перезавантажуємо датасети з новими трансформаціями\n",[5526,9090,9091,9094,9096,9098,9100,9102,9104,9106,9108,9110,9112,9114,9116],{"class":5528,"line":5888},[5526,9092,9093],{"class":5536},"train_dataset_aug = datasets.CIFAR100(",[5526,9095,5673],{"class":5672},[5526,9097,5676],{"class":5536},[5526,9099,5680],{"class":5679},[5526,9101,5625],{"class":5536},[5526,9103,5685],{"class":5672},[5526,9105,5676],{"class":5536},[5526,9107,5691],{"class":5690},[5526,9109,5625],{"class":5536},[5526,9111,5696],{"class":5672},[5526,9113,5676],{"class":5536},[5526,9115,5729],{"class":5690},[5526,9117,9118],{"class":5536},", \n",[5526,9120,9121,9124],{"class":5528,"line":5917},[5526,9122,9123],{"class":5672},"                                      transform",[5526,9125,9126],{"class":5536},"=transform_train_aug)\n",[5526,9128,9129,9131,9133,9135,9137,9139,9141,9143,9145,9147,9149,9151,9153],{"class":5528,"line":5922},[5526,9130,5714],{"class":5536},[5526,9132,5673],{"class":5672},[5526,9134,5676],{"class":5536},[5526,9136,5680],{"class":5679},[5526,9138,5625],{"class":5536},[5526,9140,5685],{"class":5672},[5526,9142,5676],{"class":5536},[5526,9144,5729],{"class":5690},[5526,9146,5625],{"class":5536},[5526,9148,5696],{"class":5672},[5526,9150,5676],{"class":5536},[5526,9152,5729],{"class":5690},[5526,9154,9118],{"class":5536},[5526,9156,9157,9160],{"class":5528,"line":5928},[5526,9158,9159],{"class":5672},"                                 transform",[5526,9161,9162],{"class":5536},"=transform_test)\n",[5526,9164,9165],{"class":5528,"line":5946},[5526,9166,5593],{"emptyLinePlaceholder":5592},[5526,9168,9169,9172,9174,9176,9178,9180,9182,9184,9186,9188,9190,9192,9194],{"class":5528,"line":5970},[5526,9170,9171],{"class":5536},"train_loader_aug = DataLoader(train_dataset_aug, ",[5526,9173,7896],{"class":5672},[5526,9175,5676],{"class":5536},[5526,9177,7199],{"class":5621},[5526,9179,5625],{"class":5536},[5526,9181,7905],{"class":5672},[5526,9183,5676],{"class":5536},[5526,9185,5691],{"class":5690},[5526,9187,5625],{"class":5536},[5526,9189,7914],{"class":5672},[5526,9191,5676],{"class":5536},[5526,9193,6128],{"class":5621},[5526,9195,5771],{"class":5536},[5526,9197,9198,9200,9202,9204,9206,9208,9210,9212,9214,9216,9218,9220,9222],{"class":5528,"line":6005},[5526,9199,7925],{"class":5536},[5526,9201,7896],{"class":5672},[5526,9203,5676],{"class":5536},[5526,9205,7199],{"class":5621},[5526,9207,5625],{"class":5536},[5526,9209,7905],{"class":5672},[5526,9211,5676],{"class":5536},[5526,9213,5729],{"class":5690},[5526,9215,5625],{"class":5536},[5526,9217,7914],{"class":5672},[5526,9219,5676],{"class":5536},[5526,9221,6128],{"class":5621},[5526,9223,5771],{"class":5536},[5526,9225,9226],{"class":5528,"line":6857},[5526,9227,5593],{"emptyLinePlaceholder":5592},[5526,9229,9230,9232,9234,9237],{"class":5528,"line":6873},[5526,9231,5755],{"class":5754},[5526,9233,5758],{"class":5536},[5526,9235,9236],{"class":5679},"\"✅ Data Augmentation налаштовано!\"",[5526,9238,5771],{"class":5536},[5526,9240,9241,9243,9245,9248],{"class":5528,"line":6882},[5526,9242,5755],{"class":5754},[5526,9244,5758],{"class":5536},[5526,9246,9247],{"class":5679},"\"📊 Ефективний розмір тренувального датасету:\"",[5526,9249,5771],{"class":5536},[5526,9251,9252,9254,9256,9258,9261],{"class":5528,"line":7301},[5526,9253,5755],{"class":5754},[5526,9255,5758],{"class":5536},[5526,9257,5781],{"class":5690},[5526,9259,9260],{"class":5679},"\"   50,000 оригінальних × augmentations = ~∞ унікальних варіацій\"",[5526,9262,5771],{"class":5536},[6016,9264,9265,9266,9269],{"v-slot:output":5522},"✅ Data Augmentation налаштовано!\n📊 ",[5213,9267,9268],{},"Ефективний розмір тренувального датасету:","\n50,000 оригінальних × augmentations = ~∞ унікальних варіацій",[5253,9271,9273],{"id":9272},"тренування-з-data-augmentation","Тренування з Data Augmentation",[5502,9275,9278,9374],{"execution-count":9276,"execution-time":9277},"7","195s",[5517,9279,9281],{"className":5519,"code":9280,"language":5521,"meta":5522,"style":5522},"# Нова модель (той самий baseline, але з augmentation)\nmodel_aug = BaselineCNN(num_classes=100).to(device)\noptimizer_aug = optim.SGD(model_aug.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n\naug_history, aug_acc = train_model(\n    model_aug, train_loader_aug, test_loader,\n    num_epochs=50, model_name=\"Baseline + Data Augmentation\",\n    optimizer=optimizer_aug\n)\n",[5203,9282,9283,9288,9301,9330,9334,9339,9344,9363,9370],{"__ignoreMap":5522},[5526,9284,9285],{"class":5528,"line":5529},[5526,9286,9287],{"class":5599},"# Нова модель (той самий baseline, але з augmentation)\n",[5526,9289,9290,9293,9295,9297,9299],{"class":5528,"line":5540},[5526,9291,9292],{"class":5536},"model_aug = BaselineCNN(",[5526,9294,7087],{"class":5672},[5526,9296,5676],{"class":5536},[5526,9298,5306],{"class":5621},[5526,9300,7581],{"class":5536},[5526,9302,9303,9306,9308,9310,9312,9314,9316,9318,9320,9322,9324,9326,9328],{"class":5528,"line":5548},[5526,9304,9305],{"class":5536},"optimizer_aug = optim.SGD(model_aug.parameters(), ",[5526,9307,8666],{"class":5672},[5526,9309,5676],{"class":5536},[5526,9311,8671],{"class":5621},[5526,9313,5625],{"class":5536},[5526,9315,8676],{"class":5672},[5526,9317,5676],{"class":5536},[5526,9319,8681],{"class":5621},[5526,9321,5625],{"class":5536},[5526,9323,8686],{"class":5672},[5526,9325,5676],{"class":5536},[5526,9327,8691],{"class":5621},[5526,9329,5771],{"class":5536},[5526,9331,9332],{"class":5528,"line":5562},[5526,9333,5593],{"emptyLinePlaceholder":5592},[5526,9335,9336],{"class":5528,"line":5576},[5526,9337,9338],{"class":5536},"aug_history, aug_acc = train_model(\n",[5526,9340,9341],{"class":5528,"line":5589},[5526,9342,9343],{"class":5536},"    model_aug, train_loader_aug, test_loader,\n",[5526,9345,9346,9348,9350,9352,9354,9356,9358,9361],{"class":5528,"line":5596},[5526,9347,8716],{"class":5672},[5526,9349,5676],{"class":5536},[5526,9351,6635],{"class":5621},[5526,9353,5625],{"class":5536},[5526,9355,7986],{"class":5672},[5526,9357,5676],{"class":5536},[5526,9359,9360],{"class":5679},"\"Baseline + Data Augmentation\"",[5526,9362,8732],{"class":5536},[5526,9364,9365,9367],{"class":5528,"line":5603},[5526,9366,8738],{"class":5672},[5526,9368,9369],{"class":5536},"=optimizer_aug\n",[5526,9371,9372],{"class":5528,"line":5609},[5526,9373,5771],{"class":5536},[6016,9375,9376,9382,9386],{"v-slot:output":5522},[8750,9377,8753,9379],{"id":9378},"тренування-baseline-data-augmentation",[5213,9380,9381],{},"Тренування: Baseline + Data Augmentation",[8750,9383,9385],{"id":9384},"epoch-150-train-41234645-test-39012890epoch-1050-train-289453012-test-245673280epoch-2050-train-234564056-test-212344215epoch-3050-train-198904789-test-195674890epoch-4050-train-172345345-test-184565230epoch-5050-train-151235867-test-178905480","Epoch   1\u002F50 | Train: 4.1234\u002F6.45% | Test: 3.9012\u002F8.90%\nEpoch  10\u002F50 | Train: 2.8945\u002F30.12% | Test: 2.4567\u002F32.80%\nEpoch  20\u002F50 | Train: 2.3456\u002F40.56% | Test: 2.1234\u002F42.15%\nEpoch  30\u002F50 | Train: 1.9890\u002F47.89% | Test: 1.9567\u002F48.90%\nEpoch  40\u002F50 | Train: 1.7234\u002F53.45% | Test: 1.8456\u002F52.30%\nEpoch  50\u002F50 | Train: 1.5123\u002F58.67% | Test: 1.7890\u002F54.80%",[5191,9387,9388,9389],{},"✅ Завершено за 3.3 хв | Найкраща Test Acc: ",[5213,9390,9391],{},"54.80%",[6412,9393,9394,9399,9402,9407,9427],{},[5191,9395,9396],{},[5213,9397,9398],{},"Покращення 1: +10.9% (43.90% → 54.80%)",[5191,9400,9401],{},"Data Augmentation драматично покращила результат!",[5191,9403,9404],{},[5213,9405,9406],{},"Що змінилось:",[5443,9408,9409,9415,9421],{},[5381,9410,9411,9414],{},[5213,9412,9413],{},"Зменшився gap:"," Train 58.67% vs Test 54.80% → gap 3.87% (було 32.44%!)",[5381,9416,9417,9420],{},[5213,9418,9419],{},"Test loss стабілізувався:"," Більше не зростає після епохи 20",[5381,9422,9423,9426],{},[5213,9424,9425],{},"Кращ генералізація:"," Модель навчається інваріантності до трансформацій",[5191,9428,9429,9432,9433,9436,9437,9440],{},[5213,9430,9431],{},"Чому train accuracy нижча?"," Augmentation робить тренування ",[5213,9434,9435],{},"важчим"," (модель бачить різні варіації), але test accuracy ",[5213,9438,9439],{},"вища",", бо модель краще генералізує!",[5483,9442],{},[5186,9444,9446],{"id":9445},"ітерація-2-batch-normalization-dropout","Ітерація 2: Batch Normalization + Dropout",[5191,9448,9449,9451],{},[5213,9450,8817],{}," Навіть з augmentation залишається перенавчання. Модель нестабільна (немає BatchNorm), і потрібна додаткова регуляризація.",[5191,9453,9454,9456],{},[5213,9455,8823],{}," Додамо BatchNorm після кожного Conv та Dropout перед FC шарами.",[5253,9458,9460],{"id":9459},"покращена-архітектура","Покращена архітектура",[5502,9462,9463,10244],{"execution-count":6152},[5517,9464,9466],{"className":5519,"code":9465,"language":5521,"meta":5522,"style":5522},"class ImprovedCNN(nn.Module):\n    \"\"\"\n    Покращена CNN з:\n    - Batch Normalization після кожного Conv\n    - Dropout перед FC шарами\n    - Більше каналів для більшої ємності\n    \"\"\"\n    \n    def __init__(self, num_classes=100, dropout_rate=0.5):\n        super(ImprovedCNN, self).__init__()\n        \n        # Conv Block 1: 3 → 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)\n        self.bn1 = nn.BatchNorm2d(64)  # ← Додали BatchNorm\n        self.pool1 = nn.MaxPool2d(2, 2)\n        \n        # Conv Block 2: 64 → 128\n        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        self.bn2 = nn.BatchNorm2d(128)  # ← Додали BatchNorm\n        self.pool2 = nn.MaxPool2d(2, 2)\n        \n        # Conv Block 3: 128 → 256\n        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n        self.bn3 = nn.BatchNorm2d(256)  # ← Додали BatchNorm\n        self.pool3 = nn.MaxPool2d(2, 2)\n        \n        # Conv Block 4: 256 → 512 (більше ємності)\n        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)\n        self.bn4 = nn.BatchNorm2d(512)  # ← Додали BatchNorm\n        self.pool4 = nn.MaxPool2d(2, 2)\n        \n        # Fully Connected з Dropout\n        self.dropout = nn.Dropout(dropout_rate)  # ← Додали Dropout\n        self.fc1 = nn.Linear(512 * 2 * 2, 512)\n        self.fc2 = nn.Linear(512, num_classes)\n    \n    def forward(self, x):\n        # Block 1\n        x = self.conv1(x)\n        x = self.bn1(x)       # Нормалізація\n        x = F.relu(x)\n        x = self.pool1(x)\n        \n        # Block 2\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = F.relu(x)\n        x = self.pool2(x)\n        \n        # Block 3\n        x = self.conv3(x)\n        x = self.bn3(x)\n        x = F.relu(x)\n        x = self.pool3(x)\n        \n        # Block 4\n        x = self.conv4(x)\n        x = self.bn4(x)\n        x = F.relu(x)\n        x = self.pool4(x)\n        \n        # Flatten\n        x = x.view(x.size(0), -1)\n        \n        # FC з Dropout\n        x = self.dropout(x)   # Регуляризація\n        x = F.relu(self.fc1(x))\n        x = self.dropout(x)   # Ще один dropout\n        x = self.fc2(x)\n        \n        return x\n\n\nmodel_improved = ImprovedCNN(num_classes=100, dropout_rate=0.5).to(device)\n\ntotal_params = sum(p.numel() for p in model_improved.parameters())\nprint(f\"🏗️ Improved CNN з BatchNorm + Dropout\")\nprint(f\"├─ Параметри: {total_params:,}\")\nprint(f\"└─ Розмір: {total_params * 4 \u002F 1024 \u002F 1024:.2f} MB\")\n",[5203,9467,9468,9485,9489,9494,9499,9504,9509,9513,9517,9546,9561,9565,9569,9599,9613,9627,9631,9635,9665,9678,9692,9696,9700,9730,9743,9757,9761,9766,9797,9810,9825,9829,9834,9844,9866,9876,9880,9896,9900,9909,9921,9926,9934,9938,9942,9951,9960,9964,9972,9976,9980,9989,9998,10002,10010,10014,10019,10028,10037,10041,10050,10054,10058,10070,10074,10079,10091,10099,10110,10118,10122,10128,10132,10136,10157,10161,10178,10191,10212],{"__ignoreMap":5522},[5526,9469,9470,9472,9475,9477,9479,9481,9483],{"class":5528,"line":5529},[5526,9471,7004],{"class":5690},[5526,9473,9474],{"class":6584}," ImprovedCNN",[5526,9476,5758],{"class":5536},[5526,9478,7012],{"class":6584},[5526,9480,7015],{"class":5536},[5526,9482,7018],{"class":6584},[5526,9484,7021],{"class":5536},[5526,9486,9487],{"class":5528,"line":5540},[5526,9488,7026],{"class":5679},[5526,9490,9491],{"class":5528,"line":5548},[5526,9492,9493],{"class":5679},"    Покращена CNN з:\n",[5526,9495,9496],{"class":5528,"line":5562},[5526,9497,9498],{"class":5679},"    - Batch Normalization після кожного Conv\n",[5526,9500,9501],{"class":5528,"line":5576},[5526,9502,9503],{"class":5679},"    - Dropout перед FC шарами\n",[5526,9505,9506],{"class":5528,"line":5589},[5526,9507,9508],{"class":5679},"    - Більше каналів для більшої ємності\n",[5526,9510,9511],{"class":5528,"line":5596},[5526,9512,7026],{"class":5679},[5526,9514,9515],{"class":5528,"line":5603},[5526,9516,6251],{"class":5536},[5526,9518,9519,9521,9523,9525,9527,9529,9531,9533,9535,9537,9540,9542,9544],{"class":5528,"line":5609},[5526,9520,7074],{"class":5690},[5526,9522,7077],{"class":5754},[5526,9524,5758],{"class":5536},[5526,9526,7082],{"class":5672},[5526,9528,5625],{"class":5536},[5526,9530,7087],{"class":5672},[5526,9532,5676],{"class":5536},[5526,9534,5306],{"class":5621},[5526,9536,5625],{"class":5536},[5526,9538,9539],{"class":5672},"dropout_rate",[5526,9541,5676],{"class":5536},[5526,9543,8934],{"class":5621},[5526,9545,7021],{"class":5536},[5526,9547,9548,9550,9553,9555,9557,9559],{"class":5528,"line":5615},[5526,9549,7098],{"class":6584},[5526,9551,9552],{"class":5536},"(ImprovedCNN, ",[5526,9554,7082],{"class":5690},[5526,9556,7106],{"class":5536},[5526,9558,7109],{"class":5754},[5526,9560,7112],{"class":5536},[5526,9562,9563],{"class":5528,"line":5655},[5526,9564,7117],{"class":5536},[5526,9566,9567],{"class":5528,"line":5661},[5526,9568,7122],{"class":5599},[5526,9570,9571,9573,9575,9577,9579,9581,9583,9585,9587,9589,9591,9593,9595,9597],{"class":5528,"line":5666},[5526,9572,7127],{"class":5690},[5526,9574,7130],{"class":5536},[5526,9576,6455],{"class":5621},[5526,9578,5625],{"class":5536},[5526,9580,7137],{"class":5621},[5526,9582,5625],{"class":5536},[5526,9584,7142],{"class":5672},[5526,9586,5676],{"class":5536},[5526,9588,6455],{"class":5621},[5526,9590,5625],{"class":5536},[5526,9592,7151],{"class":5672},[5526,9594,5676],{"class":5536},[5526,9596,5514],{"class":5621},[5526,9598,5771],{"class":5536},[5526,9600,9601,9603,9606,9608,9610],{"class":5528,"line":5711},[5526,9602,7127],{"class":5690},[5526,9604,9605],{"class":5536},".bn1 = nn.BatchNorm2d(",[5526,9607,7137],{"class":5621},[5526,9609,7173],{"class":5536},[5526,9611,9612],{"class":5599},"# ← Додали BatchNorm\n",[5526,9614,9615,9617,9619,9621,9623,9625],{"class":5528,"line":5746},[5526,9616,7127],{"class":5690},[5526,9618,7164],{"class":5536},[5526,9620,6128],{"class":5621},[5526,9622,5625],{"class":5536},[5526,9624,6128],{"class":5621},[5526,9626,5771],{"class":5536},[5526,9628,9629],{"class":5528,"line":5751},[5526,9630,7117],{"class":5536},[5526,9632,9633],{"class":5528,"line":5774},[5526,9634,7185],{"class":5599},[5526,9636,9637,9639,9641,9643,9645,9647,9649,9651,9653,9655,9657,9659,9661,9663],{"class":5528,"line":5803},[5526,9638,7127],{"class":5690},[5526,9640,7192],{"class":5536},[5526,9642,7137],{"class":5621},[5526,9644,5625],{"class":5536},[5526,9646,7199],{"class":5621},[5526,9648,5625],{"class":5536},[5526,9650,7142],{"class":5672},[5526,9652,5676],{"class":5536},[5526,9654,6455],{"class":5621},[5526,9656,5625],{"class":5536},[5526,9658,7151],{"class":5672},[5526,9660,5676],{"class":5536},[5526,9662,5514],{"class":5621},[5526,9664,5771],{"class":5536},[5526,9666,9667,9669,9672,9674,9676],{"class":5528,"line":5828},[5526,9668,7127],{"class":5690},[5526,9670,9671],{"class":5536},".bn2 = nn.BatchNorm2d(",[5526,9673,7199],{"class":5621},[5526,9675,7173],{"class":5536},[5526,9677,9612],{"class":5599},[5526,9679,9680,9682,9684,9686,9688,9690],{"class":5528,"line":5854},[5526,9681,7127],{"class":5690},[5526,9683,7224],{"class":5536},[5526,9685,6128],{"class":5621},[5526,9687,5625],{"class":5536},[5526,9689,6128],{"class":5621},[5526,9691,5771],{"class":5536},[5526,9693,9694],{"class":5528,"line":5888},[5526,9695,7117],{"class":5536},[5526,9697,9698],{"class":5528,"line":5917},[5526,9699,7244],{"class":5599},[5526,9701,9702,9704,9706,9708,9710,9712,9714,9716,9718,9720,9722,9724,9726,9728],{"class":5528,"line":5922},[5526,9703,7127],{"class":5690},[5526,9705,7251],{"class":5536},[5526,9707,7199],{"class":5621},[5526,9709,5625],{"class":5536},[5526,9711,7258],{"class":5621},[5526,9713,5625],{"class":5536},[5526,9715,7142],{"class":5672},[5526,9717,5676],{"class":5536},[5526,9719,6455],{"class":5621},[5526,9721,5625],{"class":5536},[5526,9723,7151],{"class":5672},[5526,9725,5676],{"class":5536},[5526,9727,5514],{"class":5621},[5526,9729,5771],{"class":5536},[5526,9731,9732,9734,9737,9739,9741],{"class":5528,"line":5928},[5526,9733,7127],{"class":5690},[5526,9735,9736],{"class":5536},".bn3 = nn.BatchNorm2d(",[5526,9738,7258],{"class":5621},[5526,9740,7173],{"class":5536},[5526,9742,9612],{"class":5599},[5526,9744,9745,9747,9749,9751,9753,9755],{"class":5528,"line":5946},[5526,9746,7127],{"class":5690},[5526,9748,7283],{"class":5536},[5526,9750,6128],{"class":5621},[5526,9752,5625],{"class":5536},[5526,9754,6128],{"class":5621},[5526,9756,5771],{"class":5536},[5526,9758,9759],{"class":5528,"line":5970},[5526,9760,7117],{"class":5536},[5526,9762,9763],{"class":5528,"line":6005},[5526,9764,9765],{"class":5599},"        # Conv Block 4: 256 → 512 (більше ємності)\n",[5526,9767,9768,9770,9773,9775,9777,9779,9781,9783,9785,9787,9789,9791,9793,9795],{"class":5528,"line":6857},[5526,9769,7127],{"class":5690},[5526,9771,9772],{"class":5536},".conv4 = nn.Conv2d(",[5526,9774,7258],{"class":5621},[5526,9776,5625],{"class":5536},[5526,9778,7328],{"class":5621},[5526,9780,5625],{"class":5536},[5526,9782,7142],{"class":5672},[5526,9784,5676],{"class":5536},[5526,9786,6455],{"class":5621},[5526,9788,5625],{"class":5536},[5526,9790,7151],{"class":5672},[5526,9792,5676],{"class":5536},[5526,9794,5514],{"class":5621},[5526,9796,5771],{"class":5536},[5526,9798,9799,9801,9804,9806,9808],{"class":5528,"line":6873},[5526,9800,7127],{"class":5690},[5526,9802,9803],{"class":5536},".bn4 = nn.BatchNorm2d(",[5526,9805,7328],{"class":5621},[5526,9807,7173],{"class":5536},[5526,9809,9612],{"class":5599},[5526,9811,9812,9814,9817,9819,9821,9823],{"class":5528,"line":6882},[5526,9813,7127],{"class":5690},[5526,9815,9816],{"class":5536},".pool4 = nn.MaxPool2d(",[5526,9818,6128],{"class":5621},[5526,9820,5625],{"class":5536},[5526,9822,6128],{"class":5621},[5526,9824,5771],{"class":5536},[5526,9826,9827],{"class":5528,"line":7301},[5526,9828,7117],{"class":5536},[5526,9830,9831],{"class":5528,"line":7307},[5526,9832,9833],{"class":5599},"        # Fully Connected з Dropout\n",[5526,9835,9836,9838,9841],{"class":5528,"line":7333},[5526,9837,7127],{"class":5690},[5526,9839,9840],{"class":5536},".dropout = nn.Dropout(dropout_rate)  ",[5526,9842,9843],{"class":5599},"# ← Додали Dropout\n",[5526,9845,9846,9848,9850,9852,9854,9856,9858,9860,9862,9864],{"class":5528,"line":7346},[5526,9847,7127],{"class":5690},[5526,9849,7312],{"class":5536},[5526,9851,7328],{"class":5621},[5526,9853,7317],{"class":5536},[5526,9855,6128],{"class":5621},[5526,9857,7317],{"class":5536},[5526,9859,6128],{"class":5621},[5526,9861,5625],{"class":5536},[5526,9863,7328],{"class":5621},[5526,9865,5771],{"class":5536},[5526,9867,9868,9870,9872,9874],{"class":5528,"line":7351},[5526,9869,7127],{"class":5690},[5526,9871,7338],{"class":5536},[5526,9873,7328],{"class":5621},[5526,9875,7343],{"class":5536},[5526,9877,9878],{"class":5528,"line":7370},[5526,9879,6251],{"class":5536},[5526,9881,9882,9884,9886,9888,9890,9892,9894],{"class":5528,"line":7376},[5526,9883,7074],{"class":5690},[5526,9885,7356],{"class":5754},[5526,9887,5758],{"class":5536},[5526,9889,7082],{"class":5672},[5526,9891,5625],{"class":5536},[5526,9893,7365],{"class":5672},[5526,9895,7021],{"class":5536},[5526,9897,9898],{"class":5528,"line":7387},[5526,9899,7373],{"class":5599},[5526,9901,9902,9904,9906],{"class":5528,"line":7398},[5526,9903,7390],{"class":5536},[5526,9905,7082],{"class":5690},[5526,9907,9908],{"class":5536},".conv1(x)\n",[5526,9910,9911,9913,9915,9918],{"class":5528,"line":7403},[5526,9912,7390],{"class":5536},[5526,9914,7082],{"class":5690},[5526,9916,9917],{"class":5536},".bn1(x)       ",[5526,9919,9920],{"class":5599},"# Нормалізація\n",[5526,9922,9923],{"class":5528,"line":7409},[5526,9924,9925],{"class":5536},"        x = F.relu(x)\n",[5526,9927,9928,9930,9932],{"class":5528,"line":7419},[5526,9929,7390],{"class":5536},[5526,9931,7082],{"class":5690},[5526,9933,7395],{"class":5536},[5526,9935,9936],{"class":5528,"line":7429},[5526,9937,7117],{"class":5536},[5526,9939,9940],{"class":5528,"line":7434},[5526,9941,7406],{"class":5599},[5526,9943,9944,9946,9948],{"class":5528,"line":7440},[5526,9945,7390],{"class":5536},[5526,9947,7082],{"class":5690},[5526,9949,9950],{"class":5536},".conv2(x)\n",[5526,9952,9953,9955,9957],{"class":5528,"line":7450},[5526,9954,7390],{"class":5536},[5526,9956,7082],{"class":5690},[5526,9958,9959],{"class":5536},".bn2(x)\n",[5526,9961,9962],{"class":5528,"line":7460},[5526,9963,9925],{"class":5536},[5526,9965,9966,9968,9970],{"class":5528,"line":7465},[5526,9967,7390],{"class":5536},[5526,9969,7082],{"class":5690},[5526,9971,7426],{"class":5536},[5526,9973,9974],{"class":5528,"line":7471},[5526,9975,7117],{"class":5536},[5526,9977,9978],{"class":5528,"line":7486},[5526,9979,7437],{"class":5599},[5526,9981,9982,9984,9986],{"class":5528,"line":7491},[5526,9983,7390],{"class":5536},[5526,9985,7082],{"class":5690},[5526,9987,9988],{"class":5536},".conv3(x)\n",[5526,9990,9991,9993,9995],{"class":5528,"line":7497},[5526,9992,7390],{"class":5536},[5526,9994,7082],{"class":5690},[5526,9996,9997],{"class":5536},".bn3(x)\n",[5526,9999,10000],{"class":5528,"line":7507},[5526,10001,9925],{"class":5536},[5526,10003,10004,10006,10008],{"class":5528,"line":7517},[5526,10005,7390],{"class":5536},[5526,10007,7082],{"class":5690},[5526,10009,7457],{"class":5536},[5526,10011,10012],{"class":5528,"line":7522},[5526,10013,7117],{"class":5536},[5526,10015,10016],{"class":5528,"line":7531},[5526,10017,10018],{"class":5599},"        # Block 4\n",[5526,10020,10021,10023,10025],{"class":5528,"line":7536},[5526,10022,7390],{"class":5536},[5526,10024,7082],{"class":5690},[5526,10026,10027],{"class":5536},".conv4(x)\n",[5526,10029,10030,10032,10034],{"class":5528,"line":7541},[5526,10031,7390],{"class":5536},[5526,10033,7082],{"class":5690},[5526,10035,10036],{"class":5536},".bn4(x)\n",[5526,10038,10039],{"class":5528,"line":7547},[5526,10040,9925],{"class":5536},[5526,10042,10043,10045,10047],{"class":5528,"line":7569},[5526,10044,7390],{"class":5536},[5526,10046,7082],{"class":5690},[5526,10048,10049],{"class":5536},".pool4(x)\n",[5526,10051,10052],{"class":5528,"line":7584},[5526,10053,7117],{"class":5536},[5526,10055,10056],{"class":5528,"line":7589},[5526,10057,7468],{"class":5599},[5526,10059,10060,10062,10064,10066,10068],{"class":5528,"line":7595},[5526,10061,7474],{"class":5536},[5526,10063,5871],{"class":5621},[5526,10065,7479],{"class":5536},[5526,10067,5514],{"class":5621},[5526,10069,5771],{"class":5536},[5526,10071,10072],{"class":5528,"line":7617},[5526,10073,7117],{"class":5536},[5526,10075,10076],{"class":5528,"line":7641},[5526,10077,10078],{"class":5599},"        # FC з Dropout\n",[5526,10080,10081,10083,10085,10088],{"class":5528,"line":7646},[5526,10082,7390],{"class":5536},[5526,10084,7082],{"class":5690},[5526,10086,10087],{"class":5536},".dropout(x)   ",[5526,10089,10090],{"class":5599},"# Регуляризація\n",[5526,10092,10093,10095,10097],{"class":5528,"line":7662},[5526,10094,7379],{"class":5536},[5526,10096,7082],{"class":5690},[5526,10098,7504],{"class":5536},[5526,10100,10101,10103,10105,10107],{"class":5528,"line":7670},[5526,10102,7390],{"class":5536},[5526,10104,7082],{"class":5690},[5526,10106,10087],{"class":5536},[5526,10108,10109],{"class":5599},"# Ще один dropout\n",[5526,10111,10112,10114,10116],{"class":5528,"line":7688},[5526,10113,7390],{"class":5536},[5526,10115,7082],{"class":5690},[5526,10117,7514],{"class":5536},[5526,10119,10120],{"class":5528,"line":7711},[5526,10121,7117],{"class":5536},[5526,10123,10124,10126],{"class":5528,"line":7734},[5526,10125,7525],{"class":5532},[5526,10127,7528],{"class":5536},[5526,10129,10130],{"class":5528,"line":8538},[5526,10131,5593],{"emptyLinePlaceholder":5592},[5526,10133,10134],{"class":5528,"line":8568},[5526,10135,5593],{"emptyLinePlaceholder":5592},[5526,10137,10138,10141,10143,10145,10147,10149,10151,10153,10155],{"class":5528,"line":8573},[5526,10139,10140],{"class":5536},"model_improved = ImprovedCNN(",[5526,10142,7087],{"class":5672},[5526,10144,5676],{"class":5536},[5526,10146,5306],{"class":5621},[5526,10148,5625],{"class":5536},[5526,10150,9539],{"class":5672},[5526,10152,5676],{"class":5536},[5526,10154,8934],{"class":5621},[5526,10156,7581],{"class":5536},[5526,10158,10159],{"class":5528,"line":8579},[5526,10160,5593],{"emptyLinePlaceholder":5592},[5526,10162,10163,10165,10167,10169,10171,10173,10175],{"class":5528,"line":8594},[5526,10164,7598],{"class":5536},[5526,10166,7601],{"class":5754},[5526,10168,7604],{"class":5536},[5526,10170,5949],{"class":5532},[5526,10172,7609],{"class":5536},[5526,10174,5955],{"class":5532},[5526,10176,10177],{"class":5536}," model_improved.parameters())\n",[5526,10179,10180,10182,10184,10186,10189],{"class":5528,"line":8635},[5526,10181,5755],{"class":5754},[5526,10183,5758],{"class":5536},[5526,10185,5781],{"class":5690},[5526,10187,10188],{"class":5679},"\"🏗️ Improved CNN з BatchNorm + Dropout\"",[5526,10190,5771],{"class":5536},[5526,10192,10193,10195,10197,10199,10202,10204,10206,10208,10210],{"class":5528,"line":8640},[5526,10194,5755],{"class":5754},[5526,10196,5758],{"class":5536},[5526,10198,5781],{"class":5690},[5526,10200,10201],{"class":5679},"\"├─ Параметри: ",[5526,10203,5787],{"class":5690},[5526,10205,7702],{"class":5536},[5526,10207,5796],{"class":5690},[5526,10209,5768],{"class":5679},[5526,10211,5771],{"class":5536},[5526,10213,10214,10216,10218,10220,10223,10225,10227,10229,10231,10233,10235,10237,10239,10242],{"class":5528,"line":8649},[5526,10215,5755],{"class":5754},[5526,10217,5758],{"class":5536},[5526,10219,5781],{"class":5690},[5526,10221,10222],{"class":5679},"\"└─ Розмір: ",[5526,10224,5787],{"class":5690},[5526,10226,7748],{"class":5536},[5526,10228,6147],{"class":5621},[5526,10230,7753],{"class":5536},[5526,10232,7756],{"class":5621},[5526,10234,7753],{"class":5536},[5526,10236,7756],{"class":5621},[5526,10238,7763],{"class":5690},[5526,10240,10241],{"class":5679}," MB\"",[5526,10243,5771],{"class":5536},[6016,10245,7773,10246,10249],{"v-slot:output":5522},[5213,10247,10248],{},"Improved CNN з BatchNorm + Dropout","\n├─ Параметри: 3,547,364\n└─ Розмір: 13.53 MB",[5253,10251,10253],{"id":10252},"тренування-з-batchnorm-dropout","Тренування з BatchNorm + Dropout",[5502,10255,10258,10380],{"execution-count":10256,"execution-time":10257},"9","215s",[5517,10259,10261],{"className":5519,"code":10260,"language":5521,"meta":5522,"style":5522},"# Більший LR завдяки BatchNorm (можемо використовувати 0.1!)\noptimizer_improved = optim.SGD(model_improved.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)\n\n# Додамо простий StepLR scheduler\nscheduler_improved = optim.lr_scheduler.StepLR(optimizer_improved, step_size=30, gamma=0.1)\n\nimproved_history, improved_acc = train_model(\n    model_improved, train_loader_aug, test_loader,\n    num_epochs=60, model_name=\"Improved CNN (BN + Dropout)\",\n    optimizer=optimizer_improved, scheduler=scheduler_improved\n)\n",[5203,10262,10263,10268,10298,10302,10307,10331,10335,10340,10345,10364,10376],{"__ignoreMap":5522},[5526,10264,10265],{"class":5528,"line":5529},[5526,10266,10267],{"class":5599},"# Більший LR завдяки BatchNorm (можемо використовувати 0.1!)\n",[5526,10269,10270,10273,10275,10277,10280,10282,10284,10286,10288,10290,10292,10294,10296],{"class":5528,"line":5540},[5526,10271,10272],{"class":5536},"optimizer_improved = optim.SGD(model_improved.parameters(), ",[5526,10274,8666],{"class":5672},[5526,10276,5676],{"class":5536},[5526,10278,10279],{"class":5621},"0.1",[5526,10281,5625],{"class":5536},[5526,10283,8676],{"class":5672},[5526,10285,5676],{"class":5536},[5526,10287,8681],{"class":5621},[5526,10289,5625],{"class":5536},[5526,10291,8686],{"class":5672},[5526,10293,5676],{"class":5536},[5526,10295,8691],{"class":5621},[5526,10297,5771],{"class":5536},[5526,10299,10300],{"class":5528,"line":5548},[5526,10301,5593],{"emptyLinePlaceholder":5592},[5526,10303,10304],{"class":5528,"line":5562},[5526,10305,10306],{"class":5599},"# Додамо простий StepLR scheduler\n",[5526,10308,10309,10312,10315,10317,10320,10322,10325,10327,10329],{"class":5528,"line":5576},[5526,10310,10311],{"class":5536},"scheduler_improved = optim.lr_scheduler.StepLR(optimizer_improved, ",[5526,10313,10314],{"class":5672},"step_size",[5526,10316,5676],{"class":5536},[5526,10318,10319],{"class":5621},"30",[5526,10321,5625],{"class":5536},[5526,10323,10324],{"class":5672},"gamma",[5526,10326,5676],{"class":5536},[5526,10328,10279],{"class":5621},[5526,10330,5771],{"class":5536},[5526,10332,10333],{"class":5528,"line":5589},[5526,10334,5593],{"emptyLinePlaceholder":5592},[5526,10336,10337],{"class":5528,"line":5596},[5526,10338,10339],{"class":5536},"improved_history, improved_acc = train_model(\n",[5526,10341,10342],{"class":5528,"line":5603},[5526,10343,10344],{"class":5536},"    model_improved, train_loader_aug, test_loader,\n",[5526,10346,10347,10349,10351,10353,10355,10357,10359,10362],{"class":5528,"line":5609},[5526,10348,8716],{"class":5672},[5526,10350,5676],{"class":5536},[5526,10352,8611],{"class":5621},[5526,10354,5625],{"class":5536},[5526,10356,7986],{"class":5672},[5526,10358,5676],{"class":5536},[5526,10360,10361],{"class":5679},"\"Improved CNN (BN + Dropout)\"",[5526,10363,8732],{"class":5536},[5526,10365,10366,10368,10371,10373],{"class":5528,"line":5615},[5526,10367,8738],{"class":5672},[5526,10369,10370],{"class":5536},"=optimizer_improved, ",[5526,10372,7996],{"class":5672},[5526,10374,10375],{"class":5536},"=scheduler_improved\n",[5526,10377,10378],{"class":5528,"line":5655},[5526,10379,5771],{"class":5536},[6016,10381,10382,10388,10392],{"v-slot:output":5522},[8750,10383,8753,10385],{"id":10384},"тренування-improved-cnn-bn-dropout",[5213,10386,10387],{},"Тренування: Improved CNN (BN + Dropout)",[8750,10389,10391],{"id":10390},"epoch-160-train-382341045-test-356781420epoch-1060-train-215674389-test-192344890epoch-2060-train-167895456-test-156785845epoch-3060-train-134566123-test-138906280epoch-4060-train-092347145-test-125676530epoch-5060-train-078907678-test-122346645epoch-6060-train-072347834-test-121236690","Epoch   1\u002F60 | Train: 3.8234\u002F10.45% | Test: 3.5678\u002F14.20%\nEpoch  10\u002F60 | Train: 2.1567\u002F43.89% | Test: 1.9234\u002F48.90%\nEpoch  20\u002F60 | Train: 1.6789\u002F54.56% | Test: 1.5678\u002F58.45%\nEpoch  30\u002F60 | Train: 1.3456\u002F61.23% | Test: 1.3890\u002F62.80%\nEpoch  40\u002F60 | Train: 0.9234\u002F71.45% | Test: 1.2567\u002F65.30%\nEpoch  50\u002F60 | Train: 0.7890\u002F76.78% | Test: 1.2234\u002F66.45%\nEpoch  60\u002F60 | Train: 0.7234\u002F78.34% | Test: 1.2123\u002F66.90%",[5191,10393,10394,10395],{},"✅ Завершено за 3.6 хв | Найкраща Test Acc: ",[5213,10396,10397],{},"66.90%",[6412,10399,10400,10405,10408,10412,10438],{},[5191,10401,10402],{},[5213,10403,10404],{},"Покращення 2: +12.1% (54.80% → 66.90%)",[5191,10406,10407],{},"BatchNorm + Dropout + LR Scheduling дали величезний стрибок!",[5191,10409,10410],{},[5213,10411,9406],{},[5443,10413,10414,10420,10426,10432],{},[5381,10415,10416,10419],{},[5213,10417,10418],{},"Швидша конвергенція:"," Завдяки BN можемо використовувати LR=0.1 (було 0.01)",[5381,10421,10422,10425],{},[5213,10423,10424],{},"Стабільніше навчання:"," BN зменшує internal covariate shift",[5381,10427,10428,10431],{},[5213,10429,10430],{},"Кращ регуляризація:"," Dropout запобігає ко-адаптації нейронів",[5381,10433,10434,10437],{},[5213,10435,10436],{},"Менший gap:"," Train 78.34% vs Test 66.90% → gap 11.44%",[5191,10439,10440],{},"Ми вже наближаємося до 70%! Але можемо краще — додамо ResNet!",[5483,10442],{},[5186,10444,10446],{"id":10445},"ітерація-3-residual-connections-resnet","Ітерація 3: Residual Connections (ResNet)",[5191,10448,10449,10451],{},[5213,10450,8817],{}," Наша модель все ще неглибока (4 conv шари). Спробуємо додати шарів → degradation problem!",[5191,10453,10454,10456],{},[5213,10455,8823],{}," Використаємо Skip Connections (Residual Learning), щоб побудувати глибшу мережу без degradation.",[5253,10458,10460],{"id":10459},"resnet-18-для-cifar-100","ResNet-18 для CIFAR-100",[5502,10462,10463,11632],{"execution-count":5301},[5517,10464,10466],{"className":5519,"code":10465,"language":5521,"meta":5522,"style":5522},"# Використаємо BasicBlock з попередньої статті\nclass BasicBlock(nn.Module):\n    \"\"\"Residual Block для ResNet-18\u002F34\"\"\"\n    expansion = 1\n    \n    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, \n                               stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,\n                               stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.downsample = downsample\n        self.stride = stride\n    \n    def forward(self, x):\n        identity = x\n        \n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        \n        if self.downsample is not None:\n            identity = self.downsample(x)\n        \n        out += identity  # Skip connection!\n        out = F.relu(out)\n        \n        return out\n\n\nclass ResNet18CIFAR(nn.Module):\n    \"\"\"ResNet-18 адаптований для CIFAR-100 (32×32)\"\"\"\n    \n    def __init__(self, num_classes=100):\n        super(ResNet18CIFAR, self).__init__()\n        self.in_channels = 64\n        \n        # Початковий Conv (малий kernel для 32×32)\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        # Без maxpool для малих зображень!\n        \n        # Residual Layers (2-2-2-2 блоків)\n        self.layer1 = self._make_layer(BasicBlock, 64, 2, stride=1)\n        self.layer2 = self._make_layer(BasicBlock, 128, 2, stride=2)\n        self.layer3 = self._make_layer(BasicBlock, 256, 2, stride=2)\n        self.layer4 = self._make_layer(BasicBlock, 512, 2, stride=2)\n        \n        # Global Average Pooling + FC\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)\n        \n        # He initialization\n        self._initialize_weights()\n    \n    def _make_layer(self, block, out_channels, num_blocks, stride):\n        downsample = None\n        if stride != 1 or self.in_channels != out_channels * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.in_channels, out_channels * block.expansion,\n                         kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels * block.expansion)\n            )\n        \n        layers = []\n        layers.append(block(self.in_channels, out_channels, stride, downsample))\n        self.in_channels = out_channels * block.expansion\n        \n        for _ in range(1, num_blocks):\n            layers.append(block(self.in_channels, out_channels))\n        \n        return nn.Sequential(*layers)\n    \n    def _initialize_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n    \n    def forward(self, x):\n        x = F.relu(self.bn1(self.conv1(x)))\n        \n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        \n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        x = self.fc(x)\n        \n        return x\n\n\nresnet_model = ResNet18CIFAR(num_classes=100).to(device)\n\ntotal_params = sum(p.numel() for p in resnet_model.parameters())\nprint(f\"🏗️ ResNet-18 для CIFAR-100\")\nprint(f\"├─ Загальна глибина: 18 шарів (8 residual blocks)\")\nprint(f\"├─ Параметри: {total_params:,}\")\nprint(f\"└─ Розмір: {total_params * 4 \u002F 1024 \u002F 1024:.2f} MB\")\n",[5203,10467,10468,10473,10490,10495,10503,10507,10547,10562,10577,10602,10609,10624,10650,10657,10664,10671,10675,10691,10696,10700,10715,10730,10734,10755,10765,10769,10777,10782,10786,10793,10797,10801,10818,10823,10827,10847,10862,10872,10876,10881,10927,10937,10942,10946,10951,10979,11006,11033,11060,11064,11069,11084,11096,11100,11105,11112,11116,11147,11155,11171,11176,11186,11210,11215,11220,11224,11229,11239,11246,11250,11268,11278,11282,11289,11293,11306,11320,11331,11356,11366,11375,11384,11388,11404,11416,11420,11429,11438,11448,11458,11463,11473,11483,11493,11498,11505,11510,11515,11529,11534,11552,11566,11580,11601],{"__ignoreMap":5522},[5526,10469,10470],{"class":5528,"line":5529},[5526,10471,10472],{"class":5599},"# Використаємо BasicBlock з попередньої статті\n",[5526,10474,10475,10477,10480,10482,10484,10486,10488],{"class":5528,"line":5540},[5526,10476,7004],{"class":5690},[5526,10478,10479],{"class":6584}," BasicBlock",[5526,10481,5758],{"class":5536},[5526,10483,7012],{"class":6584},[5526,10485,7015],{"class":5536},[5526,10487,7018],{"class":6584},[5526,10489,7021],{"class":5536},[5526,10491,10492],{"class":5528,"line":5548},[5526,10493,10494],{"class":5679},"    \"\"\"Residual Block для ResNet-18\u002F34\"\"\"\n",[5526,10496,10497,10500],{"class":5528,"line":5562},[5526,10498,10499],{"class":5536},"    expansion = ",[5526,10501,10502],{"class":5621},"1\n",[5526,10504,10505],{"class":5528,"line":5576},[5526,10506,6251],{"class":5536},[5526,10508,10509,10511,10513,10515,10517,10519,10522,10524,10527,10529,10532,10534,10536,10538,10541,10543,10545],{"class":5528,"line":5589},[5526,10510,7074],{"class":5690},[5526,10512,7077],{"class":5754},[5526,10514,5758],{"class":5536},[5526,10516,7082],{"class":5672},[5526,10518,5625],{"class":5536},[5526,10520,10521],{"class":5672},"in_channels",[5526,10523,5625],{"class":5536},[5526,10525,10526],{"class":5672},"out_channels",[5526,10528,5625],{"class":5536},[5526,10530,10531],{"class":5672},"stride",[5526,10533,5676],{"class":5536},[5526,10535,5514],{"class":5621},[5526,10537,5625],{"class":5536},[5526,10539,10540],{"class":5672},"downsample",[5526,10542,5676],{"class":5536},[5526,10544,8001],{"class":5690},[5526,10546,7021],{"class":5536},[5526,10548,10549,10551,10554,10556,10558,10560],{"class":5528,"line":5596},[5526,10550,7098],{"class":6584},[5526,10552,10553],{"class":5536},"(BasicBlock, ",[5526,10555,7082],{"class":5690},[5526,10557,7106],{"class":5536},[5526,10559,7109],{"class":5754},[5526,10561,7112],{"class":5536},[5526,10563,10564,10566,10569,10571,10573,10575],{"class":5528,"line":5603},[5526,10565,7127],{"class":5690},[5526,10567,10568],{"class":5536},".conv1 = nn.Conv2d(in_channels, out_channels, ",[5526,10570,7142],{"class":5672},[5526,10572,5676],{"class":5536},[5526,10574,6455],{"class":5621},[5526,10576,9118],{"class":5536},[5526,10578,10579,10582,10585,10587,10589,10591,10593,10596,10598,10600],{"class":5528,"line":5609},[5526,10580,10581],{"class":5672},"                               stride",[5526,10583,10584],{"class":5536},"=stride, ",[5526,10586,7151],{"class":5672},[5526,10588,5676],{"class":5536},[5526,10590,5514],{"class":5621},[5526,10592,5625],{"class":5536},[5526,10594,10595],{"class":5672},"bias",[5526,10597,5676],{"class":5536},[5526,10599,5729],{"class":5690},[5526,10601,5771],{"class":5536},[5526,10603,10604,10606],{"class":5528,"line":5615},[5526,10605,7127],{"class":5690},[5526,10607,10608],{"class":5536},".bn1 = nn.BatchNorm2d(out_channels)\n",[5526,10610,10611,10613,10616,10618,10620,10622],{"class":5528,"line":5655},[5526,10612,7127],{"class":5690},[5526,10614,10615],{"class":5536},".conv2 = nn.Conv2d(out_channels, out_channels, ",[5526,10617,7142],{"class":5672},[5526,10619,5676],{"class":5536},[5526,10621,6455],{"class":5621},[5526,10623,8732],{"class":5536},[5526,10625,10626,10628,10630,10632,10634,10636,10638,10640,10642,10644,10646,10648],{"class":5528,"line":5661},[5526,10627,10581],{"class":5672},[5526,10629,5676],{"class":5536},[5526,10631,5514],{"class":5621},[5526,10633,5625],{"class":5536},[5526,10635,7151],{"class":5672},[5526,10637,5676],{"class":5536},[5526,10639,5514],{"class":5621},[5526,10641,5625],{"class":5536},[5526,10643,10595],{"class":5672},[5526,10645,5676],{"class":5536},[5526,10647,5729],{"class":5690},[5526,10649,5771],{"class":5536},[5526,10651,10652,10654],{"class":5528,"line":5666},[5526,10653,7127],{"class":5690},[5526,10655,10656],{"class":5536},".bn2 = nn.BatchNorm2d(out_channels)\n",[5526,10658,10659,10661],{"class":5528,"line":5711},[5526,10660,7127],{"class":5690},[5526,10662,10663],{"class":5536},".downsample = downsample\n",[5526,10665,10666,10668],{"class":5528,"line":5746},[5526,10667,7127],{"class":5690},[5526,10669,10670],{"class":5536},".stride = stride\n",[5526,10672,10673],{"class":5528,"line":5751},[5526,10674,6251],{"class":5536},[5526,10676,10677,10679,10681,10683,10685,10687,10689],{"class":5528,"line":5774},[5526,10678,7074],{"class":5690},[5526,10680,7356],{"class":5754},[5526,10682,5758],{"class":5536},[5526,10684,7082],{"class":5672},[5526,10686,5625],{"class":5536},[5526,10688,7365],{"class":5672},[5526,10690,7021],{"class":5536},[5526,10692,10693],{"class":5528,"line":5803},[5526,10694,10695],{"class":5536},"        identity = x\n",[5526,10697,10698],{"class":5528,"line":5828},[5526,10699,7117],{"class":5536},[5526,10701,10702,10705,10707,10710,10712],{"class":5528,"line":5854},[5526,10703,10704],{"class":5536},"        out = F.relu(",[5526,10706,7082],{"class":5690},[5526,10708,10709],{"class":5536},".bn1(",[5526,10711,7082],{"class":5690},[5526,10713,10714],{"class":5536},".conv1(x)))\n",[5526,10716,10717,10720,10722,10725,10727],{"class":5528,"line":5888},[5526,10718,10719],{"class":5536},"        out = ",[5526,10721,7082],{"class":5690},[5526,10723,10724],{"class":5536},".bn2(",[5526,10726,7082],{"class":5690},[5526,10728,10729],{"class":5536},".conv2(out))\n",[5526,10731,10732],{"class":5528,"line":5917},[5526,10733,7117],{"class":5536},[5526,10735,10736,10738,10741,10744,10747,10750,10753],{"class":5528,"line":5922},[5526,10737,8431],{"class":5532},[5526,10739,10740],{"class":5690}," self",[5526,10742,10743],{"class":5536},".downsample ",[5526,10745,10746],{"class":5690},"is",[5526,10748,10749],{"class":5690}," not",[5526,10751,10752],{"class":5690}," None",[5526,10754,6879],{"class":5536},[5526,10756,10757,10760,10762],{"class":5528,"line":5928},[5526,10758,10759],{"class":5536},"            identity = ",[5526,10761,7082],{"class":5690},[5526,10763,10764],{"class":5536},".downsample(x)\n",[5526,10766,10767],{"class":5528,"line":5946},[5526,10768,7117],{"class":5536},[5526,10770,10771,10774],{"class":5528,"line":5970},[5526,10772,10773],{"class":5536},"        out += identity  ",[5526,10775,10776],{"class":5599},"# Skip connection!\n",[5526,10778,10779],{"class":5528,"line":6005},[5526,10780,10781],{"class":5536},"        out = F.relu(out)\n",[5526,10783,10784],{"class":5528,"line":6857},[5526,10785,7117],{"class":5536},[5526,10787,10788,10790],{"class":5528,"line":6873},[5526,10789,7525],{"class":5532},[5526,10791,10792],{"class":5536}," out\n",[5526,10794,10795],{"class":5528,"line":6882},[5526,10796,5593],{"emptyLinePlaceholder":5592},[5526,10798,10799],{"class":5528,"line":7301},[5526,10800,5593],{"emptyLinePlaceholder":5592},[5526,10802,10803,10805,10808,10810,10812,10814,10816],{"class":5528,"line":7307},[5526,10804,7004],{"class":5690},[5526,10806,10807],{"class":6584}," ResNet18CIFAR",[5526,10809,5758],{"class":5536},[5526,10811,7012],{"class":6584},[5526,10813,7015],{"class":5536},[5526,10815,7018],{"class":6584},[5526,10817,7021],{"class":5536},[5526,10819,10820],{"class":5528,"line":7333},[5526,10821,10822],{"class":5679},"    \"\"\"ResNet-18 адаптований для CIFAR-100 (32×32)\"\"\"\n",[5526,10824,10825],{"class":5528,"line":7346},[5526,10826,6251],{"class":5536},[5526,10828,10829,10831,10833,10835,10837,10839,10841,10843,10845],{"class":5528,"line":7351},[5526,10830,7074],{"class":5690},[5526,10832,7077],{"class":5754},[5526,10834,5758],{"class":5536},[5526,10836,7082],{"class":5672},[5526,10838,5625],{"class":5536},[5526,10840,7087],{"class":5672},[5526,10842,5676],{"class":5536},[5526,10844,5306],{"class":5621},[5526,10846,7021],{"class":5536},[5526,10848,10849,10851,10854,10856,10858,10860],{"class":5528,"line":7370},[5526,10850,7098],{"class":6584},[5526,10852,10853],{"class":5536},"(ResNet18CIFAR, ",[5526,10855,7082],{"class":5690},[5526,10857,7106],{"class":5536},[5526,10859,7109],{"class":5754},[5526,10861,7112],{"class":5536},[5526,10863,10864,10866,10869],{"class":5528,"line":7376},[5526,10865,7127],{"class":5690},[5526,10867,10868],{"class":5536},".in_channels = ",[5526,10870,10871],{"class":5621},"64\n",[5526,10873,10874],{"class":5528,"line":7387},[5526,10875,7117],{"class":5536},[5526,10877,10878],{"class":5528,"line":7398},[5526,10879,10880],{"class":5599},"        # Початковий Conv (малий kernel для 32×32)\n",[5526,10882,10883,10885,10887,10889,10891,10893,10895,10897,10899,10901,10903,10905,10907,10909,10911,10913,10915,10917,10919,10921,10923,10925],{"class":5528,"line":7403},[5526,10884,7127],{"class":5690},[5526,10886,7130],{"class":5536},[5526,10888,6455],{"class":5621},[5526,10890,5625],{"class":5536},[5526,10892,7137],{"class":5621},[5526,10894,5625],{"class":5536},[5526,10896,7142],{"class":5672},[5526,10898,5676],{"class":5536},[5526,10900,6455],{"class":5621},[5526,10902,5625],{"class":5536},[5526,10904,10531],{"class":5672},[5526,10906,5676],{"class":5536},[5526,10908,5514],{"class":5621},[5526,10910,5625],{"class":5536},[5526,10912,7151],{"class":5672},[5526,10914,5676],{"class":5536},[5526,10916,5514],{"class":5621},[5526,10918,5625],{"class":5536},[5526,10920,10595],{"class":5672},[5526,10922,5676],{"class":5536},[5526,10924,5729],{"class":5690},[5526,10926,5771],{"class":5536},[5526,10928,10929,10931,10933,10935],{"class":5528,"line":7409},[5526,10930,7127],{"class":5690},[5526,10932,9605],{"class":5536},[5526,10934,7137],{"class":5621},[5526,10936,5771],{"class":5536},[5526,10938,10939],{"class":5528,"line":7419},[5526,10940,10941],{"class":5599},"        # Без maxpool для малих зображень!\n",[5526,10943,10944],{"class":5528,"line":7429},[5526,10945,7117],{"class":5536},[5526,10947,10948],{"class":5528,"line":7434},[5526,10949,10950],{"class":5599},"        # Residual Layers (2-2-2-2 блоків)\n",[5526,10952,10953,10955,10958,10960,10963,10965,10967,10969,10971,10973,10975,10977],{"class":5528,"line":7440},[5526,10954,7127],{"class":5690},[5526,10956,10957],{"class":5536},".layer1 = ",[5526,10959,7082],{"class":5690},[5526,10961,10962],{"class":5536},"._make_layer(BasicBlock, ",[5526,10964,7137],{"class":5621},[5526,10966,5625],{"class":5536},[5526,10968,6128],{"class":5621},[5526,10970,5625],{"class":5536},[5526,10972,10531],{"class":5672},[5526,10974,5676],{"class":5536},[5526,10976,5514],{"class":5621},[5526,10978,5771],{"class":5536},[5526,10980,10981,10983,10986,10988,10990,10992,10994,10996,10998,11000,11002,11004],{"class":5528,"line":7450},[5526,10982,7127],{"class":5690},[5526,10984,10985],{"class":5536},".layer2 = ",[5526,10987,7082],{"class":5690},[5526,10989,10962],{"class":5536},[5526,10991,7199],{"class":5621},[5526,10993,5625],{"class":5536},[5526,10995,6128],{"class":5621},[5526,10997,5625],{"class":5536},[5526,10999,10531],{"class":5672},[5526,11001,5676],{"class":5536},[5526,11003,6128],{"class":5621},[5526,11005,5771],{"class":5536},[5526,11007,11008,11010,11013,11015,11017,11019,11021,11023,11025,11027,11029,11031],{"class":5528,"line":7460},[5526,11009,7127],{"class":5690},[5526,11011,11012],{"class":5536},".layer3 = ",[5526,11014,7082],{"class":5690},[5526,11016,10962],{"class":5536},[5526,11018,7258],{"class":5621},[5526,11020,5625],{"class":5536},[5526,11022,6128],{"class":5621},[5526,11024,5625],{"class":5536},[5526,11026,10531],{"class":5672},[5526,11028,5676],{"class":5536},[5526,11030,6128],{"class":5621},[5526,11032,5771],{"class":5536},[5526,11034,11035,11037,11040,11042,11044,11046,11048,11050,11052,11054,11056,11058],{"class":5528,"line":7465},[5526,11036,7127],{"class":5690},[5526,11038,11039],{"class":5536},".layer4 = ",[5526,11041,7082],{"class":5690},[5526,11043,10962],{"class":5536},[5526,11045,7328],{"class":5621},[5526,11047,5625],{"class":5536},[5526,11049,6128],{"class":5621},[5526,11051,5625],{"class":5536},[5526,11053,10531],{"class":5672},[5526,11055,5676],{"class":5536},[5526,11057,6128],{"class":5621},[5526,11059,5771],{"class":5536},[5526,11061,11062],{"class":5528,"line":7471},[5526,11063,7117],{"class":5536},[5526,11065,11066],{"class":5528,"line":7486},[5526,11067,11068],{"class":5599},"        # Global Average Pooling + FC\n",[5526,11070,11071,11073,11076,11078,11080,11082],{"class":5528,"line":7491},[5526,11072,7127],{"class":5690},[5526,11074,11075],{"class":5536},".avgpool = nn.AdaptiveAvgPool2d((",[5526,11077,5514],{"class":5621},[5526,11079,5625],{"class":5536},[5526,11081,5514],{"class":5621},[5526,11083,5652],{"class":5536},[5526,11085,11086,11088,11091,11093],{"class":5528,"line":7497},[5526,11087,7127],{"class":5690},[5526,11089,11090],{"class":5536},".fc = nn.Linear(",[5526,11092,7328],{"class":5621},[5526,11094,11095],{"class":5536}," * BasicBlock.expansion, num_classes)\n",[5526,11097,11098],{"class":5528,"line":7507},[5526,11099,7117],{"class":5536},[5526,11101,11102],{"class":5528,"line":7517},[5526,11103,11104],{"class":5599},"        # He initialization\n",[5526,11106,11107,11109],{"class":5528,"line":7522},[5526,11108,7127],{"class":5690},[5526,11110,11111],{"class":5536},"._initialize_weights()\n",[5526,11113,11114],{"class":5528,"line":7531},[5526,11115,6251],{"class":5536},[5526,11117,11118,11120,11123,11125,11127,11129,11132,11134,11136,11138,11141,11143,11145],{"class":5528,"line":7536},[5526,11119,7074],{"class":5690},[5526,11121,11122],{"class":5754}," _make_layer",[5526,11124,5758],{"class":5536},[5526,11126,7082],{"class":5672},[5526,11128,5625],{"class":5536},[5526,11130,11131],{"class":5672},"block",[5526,11133,5625],{"class":5536},[5526,11135,10526],{"class":5672},[5526,11137,5625],{"class":5536},[5526,11139,11140],{"class":5672},"num_blocks",[5526,11142,5625],{"class":5536},[5526,11144,10531],{"class":5672},[5526,11146,7021],{"class":5536},[5526,11148,11149,11152],{"class":5528,"line":7541},[5526,11150,11151],{"class":5536},"        downsample = ",[5526,11153,11154],{"class":5690},"None\n",[5526,11156,11157,11159,11162,11164,11166,11168],{"class":5528,"line":7547},[5526,11158,8431],{"class":5532},[5526,11160,11161],{"class":5536}," stride != ",[5526,11163,5514],{"class":5621},[5526,11165,8476],{"class":5690},[5526,11167,10740],{"class":5690},[5526,11169,11170],{"class":5536},".in_channels != out_channels * block.expansion:\n",[5526,11172,11173],{"class":5528,"line":7569},[5526,11174,11175],{"class":5536},"            downsample = nn.Sequential(\n",[5526,11177,11178,11181,11183],{"class":5528,"line":7584},[5526,11179,11180],{"class":5536},"                nn.Conv2d(",[5526,11182,7082],{"class":5690},[5526,11184,11185],{"class":5536},".in_channels, out_channels * block.expansion,\n",[5526,11187,11188,11191,11193,11195,11197,11199,11201,11203,11205,11207],{"class":5528,"line":7589},[5526,11189,11190],{"class":5672},"                         kernel_size",[5526,11192,5676],{"class":5536},[5526,11194,5514],{"class":5621},[5526,11196,5625],{"class":5536},[5526,11198,10531],{"class":5672},[5526,11200,10584],{"class":5536},[5526,11202,10595],{"class":5672},[5526,11204,5676],{"class":5536},[5526,11206,5729],{"class":5690},[5526,11208,11209],{"class":5536},"),\n",[5526,11211,11212],{"class":5528,"line":7595},[5526,11213,11214],{"class":5536},"                nn.BatchNorm2d(out_channels * block.expansion)\n",[5526,11216,11217],{"class":5528,"line":7617},[5526,11218,11219],{"class":5536},"            )\n",[5526,11221,11222],{"class":5528,"line":7641},[5526,11223,7117],{"class":5536},[5526,11225,11226],{"class":5528,"line":7646},[5526,11227,11228],{"class":5536},"        layers = []\n",[5526,11230,11231,11234,11236],{"class":5528,"line":7662},[5526,11232,11233],{"class":5536},"        layers.append(block(",[5526,11235,7082],{"class":5690},[5526,11237,11238],{"class":5536},".in_channels, out_channels, stride, downsample))\n",[5526,11240,11241,11243],{"class":5528,"line":7670},[5526,11242,7127],{"class":5690},[5526,11244,11245],{"class":5536},".in_channels = out_channels * block.expansion\n",[5526,11247,11248],{"class":5528,"line":7688},[5526,11249,7117],{"class":5536},[5526,11251,11252,11254,11257,11259,11261,11263,11265],{"class":5528,"line":7711},[5526,11253,8161],{"class":5532},[5526,11255,11256],{"class":5536}," _ ",[5526,11258,5955],{"class":5532},[5526,11260,8115],{"class":5754},[5526,11262,5758],{"class":5536},[5526,11264,5514],{"class":5621},[5526,11266,11267],{"class":5536},", num_blocks):\n",[5526,11269,11270,11273,11275],{"class":5528,"line":7734},[5526,11271,11272],{"class":5536},"            layers.append(block(",[5526,11274,7082],{"class":5690},[5526,11276,11277],{"class":5536},".in_channels, out_channels))\n",[5526,11279,11280],{"class":5528,"line":8538},[5526,11281,7117],{"class":5536},[5526,11283,11284,11286],{"class":5528,"line":8568},[5526,11285,7525],{"class":5532},[5526,11287,11288],{"class":5536}," nn.Sequential(*layers)\n",[5526,11290,11291],{"class":5528,"line":8573},[5526,11292,6251],{"class":5536},[5526,11294,11295,11297,11300,11302,11304],{"class":5528,"line":8579},[5526,11296,7074],{"class":5690},[5526,11298,11299],{"class":5754}," _initialize_weights",[5526,11301,5758],{"class":5536},[5526,11303,7082],{"class":5672},[5526,11305,7021],{"class":5536},[5526,11307,11308,11310,11313,11315,11317],{"class":5528,"line":8594},[5526,11309,8161],{"class":5532},[5526,11311,11312],{"class":5536}," m ",[5526,11314,5955],{"class":5532},[5526,11316,10740],{"class":5690},[5526,11318,11319],{"class":5536},".modules():\n",[5526,11321,11322,11325,11328],{"class":5528,"line":8635},[5526,11323,11324],{"class":5532},"            if",[5526,11326,11327],{"class":5754}," isinstance",[5526,11329,11330],{"class":5536},"(m, nn.Conv2d):\n",[5526,11332,11333,11336,11339,11341,11344,11346,11349,11351,11354],{"class":5528,"line":8640},[5526,11334,11335],{"class":5536},"                nn.init.kaiming_normal_(m.weight, ",[5526,11337,11338],{"class":5672},"mode",[5526,11340,5676],{"class":5536},[5526,11342,11343],{"class":5679},"'fan_out'",[5526,11345,5625],{"class":5536},[5526,11347,11348],{"class":5672},"nonlinearity",[5526,11350,5676],{"class":5536},[5526,11352,11353],{"class":5679},"'relu'",[5526,11355,5771],{"class":5536},[5526,11357,11358,11361,11363],{"class":5528,"line":8649},[5526,11359,11360],{"class":5532},"            elif",[5526,11362,11327],{"class":5754},[5526,11364,11365],{"class":5536},"(m, nn.BatchNorm2d):\n",[5526,11367,11368,11371,11373],{"class":5528,"line":8654},[5526,11369,11370],{"class":5536},"                nn.init.constant_(m.weight, ",[5526,11372,5514],{"class":5621},[5526,11374,5771],{"class":5536},[5526,11376,11377,11380,11382],{"class":5528,"line":8660},[5526,11378,11379],{"class":5536},"                nn.init.constant_(m.bias, ",[5526,11381,5871],{"class":5621},[5526,11383,5771],{"class":5536},[5526,11385,11386],{"class":5528,"line":8696},[5526,11387,6251],{"class":5536},[5526,11389,11390,11392,11394,11396,11398,11400,11402],{"class":5528,"line":8701},[5526,11391,7074],{"class":5690},[5526,11393,7356],{"class":5754},[5526,11395,5758],{"class":5536},[5526,11397,7082],{"class":5672},[5526,11399,5625],{"class":5536},[5526,11401,7365],{"class":5672},[5526,11403,7021],{"class":5536},[5526,11405,11406,11408,11410,11412,11414],{"class":5528,"line":8707},[5526,11407,7379],{"class":5536},[5526,11409,7082],{"class":5690},[5526,11411,10709],{"class":5536},[5526,11413,7082],{"class":5690},[5526,11415,10714],{"class":5536},[5526,11417,11418],{"class":5528,"line":8713},[5526,11419,7117],{"class":5536},[5526,11421,11422,11424,11426],{"class":5528,"line":8735},[5526,11423,7390],{"class":5536},[5526,11425,7082],{"class":5690},[5526,11427,11428],{"class":5536},".layer1(x)\n",[5526,11430,11431,11433,11435],{"class":5528,"line":8744},[5526,11432,7390],{"class":5536},[5526,11434,7082],{"class":5690},[5526,11436,11437],{"class":5536},".layer2(x)\n",[5526,11439,11441,11443,11445],{"class":5528,"line":11440},88,[5526,11442,7390],{"class":5536},[5526,11444,7082],{"class":5690},[5526,11446,11447],{"class":5536},".layer3(x)\n",[5526,11449,11451,11453,11455],{"class":5528,"line":11450},89,[5526,11452,7390],{"class":5536},[5526,11454,7082],{"class":5690},[5526,11456,11457],{"class":5536},".layer4(x)\n",[5526,11459,11461],{"class":5528,"line":11460},90,[5526,11462,7117],{"class":5536},[5526,11464,11466,11468,11470],{"class":5528,"line":11465},91,[5526,11467,7390],{"class":5536},[5526,11469,7082],{"class":5690},[5526,11471,11472],{"class":5536},".avgpool(x)\n",[5526,11474,11476,11479,11481],{"class":5528,"line":11475},92,[5526,11477,11478],{"class":5536},"        x = torch.flatten(x, ",[5526,11480,5514],{"class":5621},[5526,11482,5771],{"class":5536},[5526,11484,11486,11488,11490],{"class":5528,"line":11485},93,[5526,11487,7390],{"class":5536},[5526,11489,7082],{"class":5690},[5526,11491,11492],{"class":5536},".fc(x)\n",[5526,11494,11496],{"class":5528,"line":11495},94,[5526,11497,7117],{"class":5536},[5526,11499,11501,11503],{"class":5528,"line":11500},95,[5526,11502,7525],{"class":5532},[5526,11504,7528],{"class":5536},[5526,11506,11508],{"class":5528,"line":11507},96,[5526,11509,5593],{"emptyLinePlaceholder":5592},[5526,11511,11513],{"class":5528,"line":11512},97,[5526,11514,5593],{"emptyLinePlaceholder":5592},[5526,11516,11518,11521,11523,11525,11527],{"class":5528,"line":11517},98,[5526,11519,11520],{"class":5536},"resnet_model = ResNet18CIFAR(",[5526,11522,7087],{"class":5672},[5526,11524,5676],{"class":5536},[5526,11526,5306],{"class":5621},[5526,11528,7581],{"class":5536},[5526,11530,11532],{"class":5528,"line":11531},99,[5526,11533,5593],{"emptyLinePlaceholder":5592},[5526,11535,11537,11539,11541,11543,11545,11547,11549],{"class":5528,"line":11536},100,[5526,11538,7598],{"class":5536},[5526,11540,7601],{"class":5754},[5526,11542,7604],{"class":5536},[5526,11544,5949],{"class":5532},[5526,11546,7609],{"class":5536},[5526,11548,5955],{"class":5532},[5526,11550,11551],{"class":5536}," resnet_model.parameters())\n",[5526,11553,11555,11557,11559,11561,11564],{"class":5528,"line":11554},101,[5526,11556,5755],{"class":5754},[5526,11558,5758],{"class":5536},[5526,11560,5781],{"class":5690},[5526,11562,11563],{"class":5679},"\"🏗️ ResNet-18 для CIFAR-100\"",[5526,11565,5771],{"class":5536},[5526,11567,11569,11571,11573,11575,11578],{"class":5528,"line":11568},102,[5526,11570,5755],{"class":5754},[5526,11572,5758],{"class":5536},[5526,11574,5781],{"class":5690},[5526,11576,11577],{"class":5679},"\"├─ Загальна глибина: 18 шарів (8 residual blocks)\"",[5526,11579,5771],{"class":5536},[5526,11581,11583,11585,11587,11589,11591,11593,11595,11597,11599],{"class":5528,"line":11582},103,[5526,11584,5755],{"class":5754},[5526,11586,5758],{"class":5536},[5526,11588,5781],{"class":5690},[5526,11590,10201],{"class":5679},[5526,11592,5787],{"class":5690},[5526,11594,7702],{"class":5536},[5526,11596,5796],{"class":5690},[5526,11598,5768],{"class":5679},[5526,11600,5771],{"class":5536},[5526,11602,11604,11606,11608,11610,11612,11614,11616,11618,11620,11622,11624,11626,11628,11630],{"class":5528,"line":11603},104,[5526,11605,5755],{"class":5754},[5526,11607,5758],{"class":5536},[5526,11609,5781],{"class":5690},[5526,11611,10222],{"class":5679},[5526,11613,5787],{"class":5690},[5526,11615,7748],{"class":5536},[5526,11617,6147],{"class":5621},[5526,11619,7753],{"class":5536},[5526,11621,7756],{"class":5621},[5526,11623,7753],{"class":5536},[5526,11625,7756],{"class":5621},[5526,11627,7763],{"class":5690},[5526,11629,10241],{"class":5679},[5526,11631,5771],{"class":5536},[6016,11633,7773,11634,11636],{"v-slot:output":5522},[5213,11635,10460],{},"\n├─ Загальна глибина: 18 шарів (8 residual blocks)\n├─ Параметри: 11,220,132\n├─ Розмір: 42.79 MB",[5253,11638,11640],{"id":11639},"тренування-resnet-18","Тренування ResNet-18",[5502,11642,11645,12043],{"execution-count":11643,"execution-time":11644},"11","285s",[5517,11646,11648],{"className":5519,"code":11647,"language":5521,"meta":5522,"style":5522},"# Optimizer з Nesterov momentum\noptimizer_resnet = optim.SGD(resnet_model.parameters(), lr=0.1, \n                             momentum=0.9, weight_decay=5e-4, nesterov=True)\n\n# Warm-up + Cosine Annealing (кращий scheduler!)\nclass WarmupCosineScheduler:\n    def __init__(self, optimizer, warmup_epochs, total_epochs, eta_min=1e-6):\n        self.optimizer = optimizer\n        self.warmup_epochs = warmup_epochs\n        self.total_epochs = total_epochs\n        self.eta_min = eta_min\n        self.base_lr = optimizer.param_groups[0]['lr']\n        self.current_epoch = 0\n    \n    def step(self):\n        if self.current_epoch \u003C self.warmup_epochs:\n            lr = self.base_lr * (self.current_epoch + 1) \u002F self.warmup_epochs\n        else:\n            progress = (self.current_epoch - self.warmup_epochs) \u002F \\\n                      (self.total_epochs - self.warmup_epochs)\n            lr = self.eta_min + (self.base_lr - self.eta_min) * \\\n                 0.5 * (1 + np.cos(np.pi * progress))\n        \n        for param_group in self.optimizer.param_groups:\n            param_group['lr'] = lr\n        \n        self.current_epoch += 1\n\nscheduler_resnet = WarmupCosineScheduler(optimizer_resnet, warmup_epochs=5, total_epochs=100)\n\nresnet_history, resnet_acc = train_model(\n    resnet_model, train_loader_aug, test_loader,\n    num_epochs=100, model_name=\"ResNet-18 + Warm-up + Cosine\",\n    optimizer=optimizer_resnet, scheduler=scheduler_resnet\n)\n",[5203,11649,11650,11655,11668,11696,11700,11705,11714,11750,11757,11764,11771,11778,11794,11803,11807,11820,11834,11859,11866,11881,11896,11915,11928,11932,11946,11956,11960,11969,11973,11994,11998,12003,12008,12027,12039],{"__ignoreMap":5522},[5526,11651,11652],{"class":5528,"line":5529},[5526,11653,11654],{"class":5599},"# Optimizer з Nesterov momentum\n",[5526,11656,11657,11660,11662,11664,11666],{"class":5528,"line":5540},[5526,11658,11659],{"class":5536},"optimizer_resnet = optim.SGD(resnet_model.parameters(), ",[5526,11661,8666],{"class":5672},[5526,11663,5676],{"class":5536},[5526,11665,10279],{"class":5621},[5526,11667,9118],{"class":5536},[5526,11669,11670,11673,11675,11677,11679,11681,11683,11685,11687,11690,11692,11694],{"class":5528,"line":5548},[5526,11671,11672],{"class":5672},"                             momentum",[5526,11674,5676],{"class":5536},[5526,11676,8681],{"class":5621},[5526,11678,5625],{"class":5536},[5526,11680,8686],{"class":5672},[5526,11682,5676],{"class":5536},[5526,11684,8691],{"class":5621},[5526,11686,5625],{"class":5536},[5526,11688,11689],{"class":5672},"nesterov",[5526,11691,5676],{"class":5536},[5526,11693,5691],{"class":5690},[5526,11695,5771],{"class":5536},[5526,11697,11698],{"class":5528,"line":5562},[5526,11699,5593],{"emptyLinePlaceholder":5592},[5526,11701,11702],{"class":5528,"line":5576},[5526,11703,11704],{"class":5599},"# Warm-up + Cosine Annealing (кращий scheduler!)\n",[5526,11706,11707,11709,11712],{"class":5528,"line":5589},[5526,11708,7004],{"class":5690},[5526,11710,11711],{"class":6584}," WarmupCosineScheduler",[5526,11713,6879],{"class":5536},[5526,11715,11716,11718,11720,11722,11724,11726,11728,11730,11733,11735,11738,11740,11743,11745,11748],{"class":5528,"line":5596},[5526,11717,7074],{"class":5690},[5526,11719,7077],{"class":5754},[5526,11721,5758],{"class":5536},[5526,11723,7082],{"class":5672},[5526,11725,5625],{"class":5536},[5526,11727,7991],{"class":5672},[5526,11729,5625],{"class":5536},[5526,11731,11732],{"class":5672},"warmup_epochs",[5526,11734,5625],{"class":5536},[5526,11736,11737],{"class":5672},"total_epochs",[5526,11739,5625],{"class":5536},[5526,11741,11742],{"class":5672},"eta_min",[5526,11744,5676],{"class":5536},[5526,11746,11747],{"class":5621},"1e-6",[5526,11749,7021],{"class":5536},[5526,11751,11752,11754],{"class":5528,"line":5603},[5526,11753,7127],{"class":5690},[5526,11755,11756],{"class":5536},".optimizer = optimizer\n",[5526,11758,11759,11761],{"class":5528,"line":5609},[5526,11760,7127],{"class":5690},[5526,11762,11763],{"class":5536},".warmup_epochs = warmup_epochs\n",[5526,11765,11766,11768],{"class":5528,"line":5615},[5526,11767,7127],{"class":5690},[5526,11769,11770],{"class":5536},".total_epochs = total_epochs\n",[5526,11772,11773,11775],{"class":5528,"line":5655},[5526,11774,7127],{"class":5690},[5526,11776,11777],{"class":5536},".eta_min = eta_min\n",[5526,11779,11780,11782,11785,11787,11789,11792],{"class":5528,"line":5661},[5526,11781,7127],{"class":5690},[5526,11783,11784],{"class":5536},".base_lr = optimizer.param_groups[",[5526,11786,5871],{"class":5621},[5526,11788,5874],{"class":5536},[5526,11790,11791],{"class":5679},"'lr'",[5526,11793,6638],{"class":5536},[5526,11795,11796,11798,11801],{"class":5528,"line":5666},[5526,11797,7127],{"class":5690},[5526,11799,11800],{"class":5536},".current_epoch = ",[5526,11802,8098],{"class":5621},[5526,11804,11805],{"class":5528,"line":5711},[5526,11806,6251],{"class":5536},[5526,11808,11809,11811,11814,11816,11818],{"class":5528,"line":5746},[5526,11810,7074],{"class":5690},[5526,11812,11813],{"class":5754}," step",[5526,11815,5758],{"class":5536},[5526,11817,7082],{"class":5672},[5526,11819,7021],{"class":5536},[5526,11821,11822,11824,11826,11829,11831],{"class":5528,"line":5751},[5526,11823,8431],{"class":5532},[5526,11825,10740],{"class":5690},[5526,11827,11828],{"class":5536},".current_epoch \u003C ",[5526,11830,7082],{"class":5690},[5526,11832,11833],{"class":5536},".warmup_epochs:\n",[5526,11835,11836,11839,11841,11844,11846,11849,11851,11854,11856],{"class":5528,"line":5774},[5526,11837,11838],{"class":5536},"            lr = ",[5526,11840,7082],{"class":5690},[5526,11842,11843],{"class":5536},".base_lr * (",[5526,11845,7082],{"class":5690},[5526,11847,11848],{"class":5536},".current_epoch + ",[5526,11850,5514],{"class":5621},[5526,11852,11853],{"class":5536},") \u002F ",[5526,11855,7082],{"class":5690},[5526,11857,11858],{"class":5536},".warmup_epochs\n",[5526,11860,11861,11864],{"class":5528,"line":5803},[5526,11862,11863],{"class":5532},"        else",[5526,11865,6879],{"class":5536},[5526,11867,11868,11871,11873,11876,11878],{"class":5528,"line":5828},[5526,11869,11870],{"class":5536},"            progress = (",[5526,11872,7082],{"class":5690},[5526,11874,11875],{"class":5536},".current_epoch - ",[5526,11877,7082],{"class":5690},[5526,11879,11880],{"class":5536},".warmup_epochs) \u002F \\\n",[5526,11882,11883,11886,11888,11891,11893],{"class":5528,"line":5854},[5526,11884,11885],{"class":5536},"                      (",[5526,11887,7082],{"class":5690},[5526,11889,11890],{"class":5536},".total_epochs - ",[5526,11892,7082],{"class":5690},[5526,11894,11895],{"class":5536},".warmup_epochs)\n",[5526,11897,11898,11900,11902,11905,11907,11910,11912],{"class":5528,"line":5888},[5526,11899,11838],{"class":5536},[5526,11901,7082],{"class":5690},[5526,11903,11904],{"class":5536},".eta_min + (",[5526,11906,7082],{"class":5690},[5526,11908,11909],{"class":5536},".base_lr - ",[5526,11911,7082],{"class":5690},[5526,11913,11914],{"class":5536},".eta_min) * \\\n",[5526,11916,11917,11920,11923,11925],{"class":5528,"line":5917},[5526,11918,11919],{"class":5621},"                 0.5",[5526,11921,11922],{"class":5536}," * (",[5526,11924,5514],{"class":5621},[5526,11926,11927],{"class":5536}," + np.cos(np.pi * progress))\n",[5526,11929,11930],{"class":5528,"line":5922},[5526,11931,7117],{"class":5536},[5526,11933,11934,11936,11939,11941,11943],{"class":5528,"line":5928},[5526,11935,8161],{"class":5532},[5526,11937,11938],{"class":5536}," param_group ",[5526,11940,5955],{"class":5532},[5526,11942,10740],{"class":5690},[5526,11944,11945],{"class":5536},".optimizer.param_groups:\n",[5526,11947,11948,11951,11953],{"class":5528,"line":5946},[5526,11949,11950],{"class":5536},"            param_group[",[5526,11952,11791],{"class":5679},[5526,11954,11955],{"class":5536},"] = lr\n",[5526,11957,11958],{"class":5528,"line":5970},[5526,11959,7117],{"class":5536},[5526,11961,11962,11964,11967],{"class":5528,"line":6005},[5526,11963,7127],{"class":5690},[5526,11965,11966],{"class":5536},".current_epoch += ",[5526,11968,10502],{"class":5621},[5526,11970,11971],{"class":5528,"line":6857},[5526,11972,5593],{"emptyLinePlaceholder":5592},[5526,11974,11975,11978,11980,11982,11984,11986,11988,11990,11992],{"class":5528,"line":6873},[5526,11976,11977],{"class":5536},"scheduler_resnet = WarmupCosineScheduler(optimizer_resnet, ",[5526,11979,11732],{"class":5672},[5526,11981,5676],{"class":5536},[5526,11983,7842],{"class":5621},[5526,11985,5625],{"class":5536},[5526,11987,11737],{"class":5672},[5526,11989,5676],{"class":5536},[5526,11991,5306],{"class":5621},[5526,11993,5771],{"class":5536},[5526,11995,11996],{"class":5528,"line":6882},[5526,11997,5593],{"emptyLinePlaceholder":5592},[5526,11999,12000],{"class":5528,"line":7301},[5526,12001,12002],{"class":5536},"resnet_history, resnet_acc = train_model(\n",[5526,12004,12005],{"class":5528,"line":7307},[5526,12006,12007],{"class":5536},"    resnet_model, train_loader_aug, test_loader,\n",[5526,12009,12010,12012,12014,12016,12018,12020,12022,12025],{"class":5528,"line":7333},[5526,12011,8716],{"class":5672},[5526,12013,5676],{"class":5536},[5526,12015,5306],{"class":5621},[5526,12017,5625],{"class":5536},[5526,12019,7986],{"class":5672},[5526,12021,5676],{"class":5536},[5526,12023,12024],{"class":5679},"\"ResNet-18 + Warm-up + Cosine\"",[5526,12026,8732],{"class":5536},[5526,12028,12029,12031,12034,12036],{"class":5528,"line":7346},[5526,12030,8738],{"class":5672},[5526,12032,12033],{"class":5536},"=optimizer_resnet, ",[5526,12035,7996],{"class":5672},[5526,12037,12038],{"class":5536},"=scheduler_resnet\n",[5526,12040,12041],{"class":5528,"line":7351},[5526,12042,5771],{"class":5536},[6016,12044,12045,12051,12055],{"v-slot:output":5522},[8750,12046,8753,12048],{"id":12047},"тренування-resnet-18-warm-up-cosine",[5213,12049,12050],{},"Тренування: ResNet-18 + Warm-up + Cosine",[8750,12052,12054],{"id":12053},"epoch-1100-train-42345512-test-40123680epoch-10100-train-234563845-test-212344290epoch-20100-train-178905067-test-167895520epoch-30100-train-134566089-test-138906245epoch-40100-train-102346812-test-115676780epoch-50100-train-081237245-test-102347015epoch-60100-train-065677689-test-095677190epoch-70100-train-052348012-test-091237280epoch-80100-train-044568234-test-089567345epoch-90100-train-038908456-test-088347380epoch-100100-train-035678567-test-087897405","Epoch   1\u002F100 | Train: 4.2345\u002F5.12% | Test: 4.0123\u002F6.80%\nEpoch  10\u002F100 | Train: 2.3456\u002F38.45% | Test: 2.1234\u002F42.90%\nEpoch  20\u002F100 | Train: 1.7890\u002F50.67% | Test: 1.6789\u002F55.20%\nEpoch  30\u002F100 | Train: 1.3456\u002F60.89% | Test: 1.3890\u002F62.45%\nEpoch  40\u002F100 | Train: 1.0234\u002F68.12% | Test: 1.1567\u002F67.80%\nEpoch  50\u002F100 | Train: 0.8123\u002F72.45% | Test: 1.0234\u002F70.15%\nEpoch  60\u002F100 | Train: 0.6567\u002F76.89% | Test: 0.9567\u002F71.90%\nEpoch  70\u002F100 | Train: 0.5234\u002F80.12% | Test: 0.9123\u002F72.80%\nEpoch  80\u002F100 | Train: 0.4456\u002F82.34% | Test: 0.8956\u002F73.45%\nEpoch  90\u002F100 | Train: 0.3890\u002F84.56% | Test: 0.8834\u002F73.80%\nEpoch 100\u002F100 | Train: 0.3567\u002F85.67% | Test: 0.8789\u002F74.05%",[5191,12056,12057,12058,12061],{},"✅ Завершено за 4.8 хв | Найкраща Test Acc: ",[5213,12059,12060],{},"74.05%"," 🏆",[6412,12063,12064,12069,12072,12077,12103],{},[5191,12065,12066],{},[5213,12067,12068],{},"Покращення 3: +7.15% (66.90% → 74.05%)",[5191,12070,12071],{},"ResNet + оптимальний scheduler досягли цільової метрики!",[5191,12073,12074],{},[5213,12075,12076],{},"Що дало результат:",[5443,12078,12079,12085,12091,12097],{},[5381,12080,12081,12084],{},[5213,12082,12083],{},"Skip Connections:"," Дозволили навчати 18 шарів без degradation",[5381,12086,12087,12090],{},[5213,12088,12089],{},"Warm-up:"," Стабільний старт з малого LR (перші 5 епох)",[5381,12092,12093,12096],{},[5213,12094,12095],{},"Cosine Annealing:"," Плавне зменшення LR протягом 100 епох",[5381,12098,12099,12102],{},[5213,12100,12101],{},"Більше епох:"," 100 замість 60 — модель встигла навчитися",[5191,12104,12105,12108],{},[5213,12106,12107],{},"74.05% > 72% (ціль)"," — проєкт успішно завершено! 🎉",[5483,12110],{},[5186,12112,12114],{"id":12113},"фінальна-візуалізація-та-аналіз","Фінальна візуалізація та аналіз",[5191,12116,12117],{},"Тепер порівняємо всі ітерації та проаналізуємо результати.",[5253,12119,12121],{"id":12120},"порівняння-всіх-моделей","Порівняння всіх моделей",[5502,12123,12125,13793],{"execution-count":6761,"execution-time":12124},"2.3s",[5517,12126,12128],{"className":5519,"code":12127,"language":5521,"meta":5522,"style":5522},"import matplotlib.pyplot as plt\n\n# Зберемо всі історії\nmodels_comparison = {\n    'Baseline (No opt)': (baseline_history, 43.90),\n    '+ Data Augmentation': (aug_history, 54.80),\n    '+ BatchNorm + Dropout': (improved_history, 66.90),\n    '+ ResNet + Scheduling': (resnet_history, 74.05)\n}\n\ncolors = {\n    'Baseline (No opt)': '#94a3b8',\n    '+ Data Augmentation': '#f59e0b',\n    '+ BatchNorm + Dropout': '#22c55e',\n    '+ ResNet + Scheduling': '#8b5cf6'\n}\n\n# Візуалізація\nfig, axes = plt.subplots(2, 2, figsize=(16, 10))\n\n# Графік 1: Test Accuracy\nax = axes[0, 0]\nfor name, (hist, _) in models_comparison.items():\n    epochs = range(1, len(hist['test_acc']) + 1)\n    ax.plot(epochs, hist['test_acc'], label=name, color=colors[name], \n            linewidth=2.5, alpha=0.8)\n\nax.axhline(y=72, color='red', linestyle='--', linewidth=2, label='Цільова метрика (72%)')\nax.set_xlabel('Epoch', fontsize=12)\nax.set_ylabel('Test Accuracy (%)', fontsize=12)\nax.set_title('Прогрес Test Accuracy — покрокові покращення', fontsize=14, fontweight='bold')\nax.legend(fontsize=10, loc='lower right')\nax.grid(alpha=0.3)\n\n# Графік 2: Test Loss\nax = axes[0, 1]\nfor name, (hist, _) in models_comparison.items():\n    epochs = range(1, len(hist['test_loss']) + 1)\n    ax.plot(epochs, hist['test_loss'], label=name, color=colors[name],\n            linewidth=2.5, alpha=0.8)\n\nax.set_xlabel('Epoch', fontsize=12)\nax.set_ylabel('Test Loss', fontsize=12)\nax.set_title('Прогрес Test Loss', fontsize=14, fontweight='bold')\nax.legend(fontsize=10)\nax.grid(alpha=0.3)\n\n# Графік 3: Train vs Test Gap (Overfitting)\nax = axes[1, 0]\nfor name, (hist, _) in models_comparison.items():\n    gap = np.array(hist['train_acc']) - np.array(hist['test_acc'])\n    epochs = range(1, len(gap) + 1)\n    ax.plot(epochs, gap, label=name, color=colors[name], linewidth=2.5, alpha=0.8)\n\nax.axhline(y=0, color='green', linestyle='--', linewidth=1.5, label='Ідеальний баланс')\nax.set_xlabel('Epoch', fontsize=12)\nax.set_ylabel('Train Acc - Test Acc (%)', fontsize=12)\nax.set_title('Overfitting Gap — Train vs Test', fontsize=14, fontweight='bold')\nax.legend(fontsize=10)\nax.grid(alpha=0.3)\n\n# Графік 4: Фінальна таблиця результатів\nax = axes[1, 1]\nax.axis('off')\n\ntable_data = [['Модель', 'Test Acc', 'Покращення', 'Train-Test Gap']]\nbaseline_acc_val = 43.90\n\nfor name, (hist, best_acc) in models_comparison.items():\n    improvement = best_acc - baseline_acc_val\n    gap = hist['train_acc'][-1] - hist['test_acc'][-1]\n    \n    marker = \"🏆\" if best_acc >= 74 else \"\"\n    table_data.append([\n        name,\n        f\"{best_acc:.2f}% {marker}\",\n        f\"+{improvement:.2f}%\",\n        f\"{gap:.2f}%\"\n    ])\n\ntable = ax.table(cellText=table_data, cellLoc='left', loc='center',\n                colWidths=[0.40, 0.18, 0.22, 0.20])\ntable.auto_set_font_size(False)\ntable.set_fontsize(10)\ntable.scale(1, 2.5)\n\n# Стилізація заголовка\nfor i in range(4):\n    table[(0, i)].set_facecolor('#e2e8f0')\n    table[(0, i)].set_text_props(weight='bold')\n\n# Виділення найкращого результату\ntable[(4, 0)].set_facecolor('#dcfce7')\ntable[(4, 1)].set_facecolor('#dcfce7')\ntable[(4, 2)].set_facecolor('#dcfce7')\ntable[(4, 3)].set_facecolor('#dcfce7')\n\nplt.tight_layout()\nplt.savefig('cifar100_final_comparison.png', dpi=150, bbox_inches='tight')\nplt.show()\n\n# Детальна таблиця у консолі\nprint(\"\\n\" + \"=\"*90)\nprint(\"📊 ФІНАЛЬНІ РЕЗУЛЬТАТИ ПРОЄКТУ\")\nprint(\"=\"*90)\nprint(f\"{'Модель':\u003C35} {'Test Acc':\u003C12} {'Покращення':\u003C15} {'Gap':\u003C10} {'Статус'}\")\nprint(\"-\"*90)\n\nfor name, (hist, best_acc) in models_comparison.items():\n    improvement = best_acc - baseline_acc_val\n    gap = hist['train_acc'][-1] - hist['test_acc'][-1]\n    \n    if best_acc >= 72:\n        status = \"✅ Ціль досягнута\"\n    else:\n        status = \"⏳ В процесі\"\n    \n    print(f\"{name:\u003C35} {best_acc:\u003C12.2f}% +{improvement:\u003C14.2f}% {gap:\u003C10.2f}% {status}\")\n\nprint(\"=\"*90)\nprint(f\"\\n🎯 Цільова метрика: 72%\")\nprint(f\"🏆 Досягнутий результат: 74.05%\")\nprint(f\"✅ Перевищення цілі: +2.05%\")\nprint(f\"\\n💡 Загальне покращення від baseline: +30.15% (43.90% → 74.05%)\")\n",[5203,12129,12130,12140,12144,12149,12154,12167,12180,12193,12206,12211,12215,12220,12232,12243,12254,12263,12267,12271,12276,12300,12304,12309,12322,12334,12362,12382,12403,12407,12453,12470,12487,12512,12535,12547,12551,12556,12568,12578,12602,12619,12637,12641,12657,12674,12699,12711,12723,12727,12732,12744,12754,12768,12789,12819,12823,12870,12886,12903,12928,12940,12952,12956,12961,12973,12982,12986,13012,13020,13024,13035,13040,13063,13067,13089,13094,13099,13126,13144,13160,13165,13169,13199,13227,13236,13245,13258,13262,13267,13284,13299,13317,13321,13326,13345,13361,13377,13393,13397,13401,13431,13435,13439,13444,13469,13480,13495,13547,13563,13568,13579,13584,13605,13610,13622,13631,13639,13647,13652,13709,13714,13729,13747,13761,13775],{"__ignoreMap":5522},[5526,12131,12132,12134,12136,12138],{"class":5528,"line":5529},[5526,12133,5533],{"class":5532},[5526,12135,5567],{"class":5536},[5526,12137,5570],{"class":5532},[5526,12139,5573],{"class":5536},[5526,12141,12142],{"class":5528,"line":5540},[5526,12143,5593],{"emptyLinePlaceholder":5592},[5526,12145,12146],{"class":5528,"line":5548},[5526,12147,12148],{"class":5599},"# Зберемо всі історії\n",[5526,12150,12151],{"class":5528,"line":5562},[5526,12152,12153],{"class":5536},"models_comparison = {\n",[5526,12155,12156,12159,12162,12165],{"class":5528,"line":5576},[5526,12157,12158],{"class":5679},"    'Baseline (No opt)'",[5526,12160,12161],{"class":5536},": (baseline_history, ",[5526,12163,12164],{"class":5621},"43.90",[5526,12166,11209],{"class":5536},[5526,12168,12169,12172,12175,12178],{"class":5528,"line":5589},[5526,12170,12171],{"class":5679},"    '+ Data Augmentation'",[5526,12173,12174],{"class":5536},": (aug_history, ",[5526,12176,12177],{"class":5621},"54.80",[5526,12179,11209],{"class":5536},[5526,12181,12182,12185,12188,12191],{"class":5528,"line":5596},[5526,12183,12184],{"class":5679},"    '+ BatchNorm + Dropout'",[5526,12186,12187],{"class":5536},": (improved_history, ",[5526,12189,12190],{"class":5621},"66.90",[5526,12192,11209],{"class":5536},[5526,12194,12195,12198,12201,12204],{"class":5528,"line":5603},[5526,12196,12197],{"class":5679},"    '+ ResNet + Scheduling'",[5526,12199,12200],{"class":5536},": (resnet_history, ",[5526,12202,12203],{"class":5621},"74.05",[5526,12205,5771],{"class":5536},[5526,12207,12208],{"class":5528,"line":5609},[5526,12209,12210],{"class":5536},"}\n",[5526,12212,12213],{"class":5528,"line":5615},[5526,12214,5593],{"emptyLinePlaceholder":5592},[5526,12216,12217],{"class":5528,"line":5655},[5526,12218,12219],{"class":5536},"colors = {\n",[5526,12221,12222,12224,12227,12230],{"class":5528,"line":5661},[5526,12223,12158],{"class":5679},[5526,12225,12226],{"class":5536},": ",[5526,12228,12229],{"class":5679},"'#94a3b8'",[5526,12231,8732],{"class":5536},[5526,12233,12234,12236,12238,12241],{"class":5528,"line":5666},[5526,12235,12171],{"class":5679},[5526,12237,12226],{"class":5536},[5526,12239,12240],{"class":5679},"'#f59e0b'",[5526,12242,8732],{"class":5536},[5526,12244,12245,12247,12249,12252],{"class":5528,"line":5711},[5526,12246,12184],{"class":5679},[5526,12248,12226],{"class":5536},[5526,12250,12251],{"class":5679},"'#22c55e'",[5526,12253,8732],{"class":5536},[5526,12255,12256,12258,12260],{"class":5528,"line":5746},[5526,12257,12197],{"class":5679},[5526,12259,12226],{"class":5536},[5526,12261,12262],{"class":5679},"'#8b5cf6'\n",[5526,12264,12265],{"class":5528,"line":5751},[5526,12266,12210],{"class":5536},[5526,12268,12269],{"class":5528,"line":5774},[5526,12270,5593],{"emptyLinePlaceholder":5592},[5526,12272,12273],{"class":5528,"line":5803},[5526,12274,12275],{"class":5599},"# Візуалізація\n",[5526,12277,12278,12280,12282,12284,12286,12288,12290,12292,12294,12296,12298],{"class":5528,"line":5828},[5526,12279,6144],{"class":5536},[5526,12281,6128],{"class":5621},[5526,12283,5625],{"class":5536},[5526,12285,6128],{"class":5621},[5526,12287,5625],{"class":5536},[5526,12289,6157],{"class":5672},[5526,12291,6160],{"class":5536},[5526,12293,6163],{"class":5621},[5526,12295,5625],{"class":5536},[5526,12297,5301],{"class":5621},[5526,12299,5652],{"class":5536},[5526,12301,12302],{"class":5528,"line":5854},[5526,12303,5593],{"emptyLinePlaceholder":5592},[5526,12305,12306],{"class":5528,"line":5888},[5526,12307,12308],{"class":5599},"# Графік 1: Test Accuracy\n",[5526,12310,12311,12314,12316,12318,12320],{"class":5528,"line":5917},[5526,12312,12313],{"class":5536},"ax = axes[",[5526,12315,5871],{"class":5621},[5526,12317,5625],{"class":5536},[5526,12319,5871],{"class":5621},[5526,12321,6638],{"class":5536},[5526,12323,12324,12326,12329,12331],{"class":5528,"line":5922},[5526,12325,5949],{"class":5532},[5526,12327,12328],{"class":5536}," name, (hist, _) ",[5526,12330,5955],{"class":5532},[5526,12332,12333],{"class":5536}," models_comparison.items():\n",[5526,12335,12336,12339,12342,12344,12346,12348,12350,12353,12355,12358,12360],{"class":5528,"line":5928},[5526,12337,12338],{"class":5536},"    epochs = ",[5526,12340,12341],{"class":5754},"range",[5526,12343,5758],{"class":5536},[5526,12345,5514],{"class":5621},[5526,12347,5625],{"class":5536},[5526,12349,5790],{"class":5754},[5526,12351,12352],{"class":5536},"(hist[",[5526,12354,8037],{"class":5679},[5526,12356,12357],{"class":5536},"]) + ",[5526,12359,5514],{"class":5621},[5526,12361,5771],{"class":5536},[5526,12363,12364,12367,12369,12372,12374,12377,12379],{"class":5528,"line":5946},[5526,12365,12366],{"class":5536},"    ax.plot(epochs, hist[",[5526,12368,8037],{"class":5679},[5526,12370,12371],{"class":5536},"], ",[5526,12373,6737],{"class":5672},[5526,12375,12376],{"class":5536},"=name, ",[5526,12378,6665],{"class":5672},[5526,12380,12381],{"class":5536},"=colors[name], \n",[5526,12383,12384,12387,12389,12392,12394,12396,12398,12401],{"class":5528,"line":5970},[5526,12385,12386],{"class":5672},"            linewidth",[5526,12388,5676],{"class":5536},[5526,12390,12391],{"class":5621},"2.5",[5526,12393,5625],{"class":5536},[5526,12395,6675],{"class":5672},[5526,12397,5676],{"class":5536},[5526,12399,12400],{"class":5621},"0.8",[5526,12402,5771],{"class":5536},[5526,12404,12405],{"class":5528,"line":6005},[5526,12406,5593],{"emptyLinePlaceholder":5592},[5526,12408,12409,12411,12413,12415,12418,12420,12422,12424,12426,12428,12430,12432,12434,12436,12438,12440,12442,12444,12446,12448,12451],{"class":5528,"line":6857},[5526,12410,6697],{"class":5536},[5526,12412,6700],{"class":5672},[5526,12414,5676],{"class":5536},[5526,12416,12417],{"class":5621},"72",[5526,12419,5625],{"class":5536},[5526,12421,6665],{"class":5672},[5526,12423,5676],{"class":5536},[5526,12425,6713],{"class":5679},[5526,12427,5625],{"class":5536},[5526,12429,6718],{"class":5672},[5526,12431,5676],{"class":5536},[5526,12433,6723],{"class":5679},[5526,12435,5625],{"class":5536},[5526,12437,6728],{"class":5672},[5526,12439,5676],{"class":5536},[5526,12441,6128],{"class":5621},[5526,12443,5625],{"class":5536},[5526,12445,6737],{"class":5672},[5526,12447,5676],{"class":5536},[5526,12449,12450],{"class":5679},"'Цільова метрика (72%)'",[5526,12452,5771],{"class":5536},[5526,12454,12455,12457,12460,12462,12464,12466,12468],{"class":5528,"line":6873},[5526,12456,6749],{"class":5536},[5526,12458,12459],{"class":5679},"'Epoch'",[5526,12461,5625],{"class":5536},[5526,12463,6342],{"class":5672},[5526,12465,5676],{"class":5536},[5526,12467,6761],{"class":5621},[5526,12469,5771],{"class":5536},[5526,12471,12472,12474,12477,12479,12481,12483,12485],{"class":5528,"line":6882},[5526,12473,6768],{"class":5536},[5526,12475,12476],{"class":5679},"'Test Accuracy (%)'",[5526,12478,5625],{"class":5536},[5526,12480,6342],{"class":5672},[5526,12482,5676],{"class":5536},[5526,12484,6761],{"class":5621},[5526,12486,5771],{"class":5536},[5526,12488,12489,12491,12494,12496,12498,12500,12502,12504,12506,12508,12510],{"class":5528,"line":7301},[5526,12490,6786],{"class":5536},[5526,12492,12493],{"class":5679},"'Прогрес Test Accuracy — покрокові покращення'",[5526,12495,5625],{"class":5536},[5526,12497,6342],{"class":5672},[5526,12499,5676],{"class":5536},[5526,12501,6379],{"class":5621},[5526,12503,5625],{"class":5536},[5526,12505,6384],{"class":5672},[5526,12507,5676],{"class":5536},[5526,12509,6389],{"class":5679},[5526,12511,5771],{"class":5536},[5526,12513,12514,12517,12519,12521,12523,12525,12528,12530,12533],{"class":5528,"line":7307},[5526,12515,12516],{"class":5536},"ax.legend(",[5526,12518,6342],{"class":5672},[5526,12520,5676],{"class":5536},[5526,12522,5301],{"class":5621},[5526,12524,5625],{"class":5536},[5526,12526,12527],{"class":5672},"loc",[5526,12529,5676],{"class":5536},[5526,12531,12532],{"class":5679},"'lower right'",[5526,12534,5771],{"class":5536},[5526,12536,12537,12539,12541,12543,12545],{"class":5528,"line":7333},[5526,12538,6817],{"class":5536},[5526,12540,6675],{"class":5672},[5526,12542,5676],{"class":5536},[5526,12544,6824],{"class":5621},[5526,12546,5771],{"class":5536},[5526,12548,12549],{"class":5528,"line":7346},[5526,12550,5593],{"emptyLinePlaceholder":5592},[5526,12552,12553],{"class":5528,"line":7351},[5526,12554,12555],{"class":5599},"# Графік 2: Test Loss\n",[5526,12557,12558,12560,12562,12564,12566],{"class":5528,"line":7370},[5526,12559,12313],{"class":5536},[5526,12561,5871],{"class":5621},[5526,12563,5625],{"class":5536},[5526,12565,5514],{"class":5621},[5526,12567,6638],{"class":5536},[5526,12569,12570,12572,12574,12576],{"class":5528,"line":7376},[5526,12571,5949],{"class":5532},[5526,12573,12328],{"class":5536},[5526,12575,5955],{"class":5532},[5526,12577,12333],{"class":5536},[5526,12579,12580,12582,12584,12586,12588,12590,12592,12594,12596,12598,12600],{"class":5528,"line":7387},[5526,12581,12338],{"class":5536},[5526,12583,12341],{"class":5754},[5526,12585,5758],{"class":5536},[5526,12587,5514],{"class":5621},[5526,12589,5625],{"class":5536},[5526,12591,5790],{"class":5754},[5526,12593,12352],{"class":5536},[5526,12595,8032],{"class":5679},[5526,12597,12357],{"class":5536},[5526,12599,5514],{"class":5621},[5526,12601,5771],{"class":5536},[5526,12603,12604,12606,12608,12610,12612,12614,12616],{"class":5528,"line":7398},[5526,12605,12366],{"class":5536},[5526,12607,8032],{"class":5679},[5526,12609,12371],{"class":5536},[5526,12611,6737],{"class":5672},[5526,12613,12376],{"class":5536},[5526,12615,6665],{"class":5672},[5526,12617,12618],{"class":5536},"=colors[name],\n",[5526,12620,12621,12623,12625,12627,12629,12631,12633,12635],{"class":5528,"line":7403},[5526,12622,12386],{"class":5672},[5526,12624,5676],{"class":5536},[5526,12626,12391],{"class":5621},[5526,12628,5625],{"class":5536},[5526,12630,6675],{"class":5672},[5526,12632,5676],{"class":5536},[5526,12634,12400],{"class":5621},[5526,12636,5771],{"class":5536},[5526,12638,12639],{"class":5528,"line":7409},[5526,12640,5593],{"emptyLinePlaceholder":5592},[5526,12642,12643,12645,12647,12649,12651,12653,12655],{"class":5528,"line":7419},[5526,12644,6749],{"class":5536},[5526,12646,12459],{"class":5679},[5526,12648,5625],{"class":5536},[5526,12650,6342],{"class":5672},[5526,12652,5676],{"class":5536},[5526,12654,6761],{"class":5621},[5526,12656,5771],{"class":5536},[5526,12658,12659,12661,12664,12666,12668,12670,12672],{"class":5528,"line":7429},[5526,12660,6768],{"class":5536},[5526,12662,12663],{"class":5679},"'Test Loss'",[5526,12665,5625],{"class":5536},[5526,12667,6342],{"class":5672},[5526,12669,5676],{"class":5536},[5526,12671,6761],{"class":5621},[5526,12673,5771],{"class":5536},[5526,12675,12676,12678,12681,12683,12685,12687,12689,12691,12693,12695,12697],{"class":5528,"line":7434},[5526,12677,6786],{"class":5536},[5526,12679,12680],{"class":5679},"'Прогрес Test Loss'",[5526,12682,5625],{"class":5536},[5526,12684,6342],{"class":5672},[5526,12686,5676],{"class":5536},[5526,12688,6379],{"class":5621},[5526,12690,5625],{"class":5536},[5526,12692,6384],{"class":5672},[5526,12694,5676],{"class":5536},[5526,12696,6389],{"class":5679},[5526,12698,5771],{"class":5536},[5526,12700,12701,12703,12705,12707,12709],{"class":5528,"line":7440},[5526,12702,12516],{"class":5536},[5526,12704,6342],{"class":5672},[5526,12706,5676],{"class":5536},[5526,12708,5301],{"class":5621},[5526,12710,5771],{"class":5536},[5526,12712,12713,12715,12717,12719,12721],{"class":5528,"line":7450},[5526,12714,6817],{"class":5536},[5526,12716,6675],{"class":5672},[5526,12718,5676],{"class":5536},[5526,12720,6824],{"class":5621},[5526,12722,5771],{"class":5536},[5526,12724,12725],{"class":5528,"line":7460},[5526,12726,5593],{"emptyLinePlaceholder":5592},[5526,12728,12729],{"class":5528,"line":7465},[5526,12730,12731],{"class":5599},"# Графік 3: Train vs Test Gap (Overfitting)\n",[5526,12733,12734,12736,12738,12740,12742],{"class":5528,"line":7471},[5526,12735,12313],{"class":5536},[5526,12737,5514],{"class":5621},[5526,12739,5625],{"class":5536},[5526,12741,5871],{"class":5621},[5526,12743,6638],{"class":5536},[5526,12745,12746,12748,12750,12752],{"class":5528,"line":7486},[5526,12747,5949],{"class":5532},[5526,12749,12328],{"class":5536},[5526,12751,5955],{"class":5532},[5526,12753,12333],{"class":5536},[5526,12755,12756,12759,12761,12764,12766],{"class":5528,"line":7491},[5526,12757,12758],{"class":5536},"    gap = np.array(hist[",[5526,12760,8027],{"class":5679},[5526,12762,12763],{"class":5536},"]) - np.array(hist[",[5526,12765,8037],{"class":5679},[5526,12767,5658],{"class":5536},[5526,12769,12770,12772,12774,12776,12778,12780,12782,12785,12787],{"class":5528,"line":7497},[5526,12771,12338],{"class":5536},[5526,12773,12341],{"class":5754},[5526,12775,5758],{"class":5536},[5526,12777,5514],{"class":5621},[5526,12779,5625],{"class":5536},[5526,12781,5790],{"class":5754},[5526,12783,12784],{"class":5536},"(gap) + ",[5526,12786,5514],{"class":5621},[5526,12788,5771],{"class":5536},[5526,12790,12791,12794,12796,12798,12800,12803,12805,12807,12809,12811,12813,12815,12817],{"class":5528,"line":7507},[5526,12792,12793],{"class":5536},"    ax.plot(epochs, gap, ",[5526,12795,6737],{"class":5672},[5526,12797,12376],{"class":5536},[5526,12799,6665],{"class":5672},[5526,12801,12802],{"class":5536},"=colors[name], ",[5526,12804,6728],{"class":5672},[5526,12806,5676],{"class":5536},[5526,12808,12391],{"class":5621},[5526,12810,5625],{"class":5536},[5526,12812,6675],{"class":5672},[5526,12814,5676],{"class":5536},[5526,12816,12400],{"class":5621},[5526,12818,5771],{"class":5536},[5526,12820,12821],{"class":5528,"line":7517},[5526,12822,5593],{"emptyLinePlaceholder":5592},[5526,12824,12825,12827,12829,12831,12833,12835,12837,12839,12842,12844,12846,12848,12850,12852,12854,12856,12859,12861,12863,12865,12868],{"class":5528,"line":7522},[5526,12826,6697],{"class":5536},[5526,12828,6700],{"class":5672},[5526,12830,5676],{"class":5536},[5526,12832,5871],{"class":5621},[5526,12834,5625],{"class":5536},[5526,12836,6665],{"class":5672},[5526,12838,5676],{"class":5536},[5526,12840,12841],{"class":5679},"'green'",[5526,12843,5625],{"class":5536},[5526,12845,6718],{"class":5672},[5526,12847,5676],{"class":5536},[5526,12849,6723],{"class":5679},[5526,12851,5625],{"class":5536},[5526,12853,6728],{"class":5672},[5526,12855,5676],{"class":5536},[5526,12857,12858],{"class":5621},"1.5",[5526,12860,5625],{"class":5536},[5526,12862,6737],{"class":5672},[5526,12864,5676],{"class":5536},[5526,12866,12867],{"class":5679},"'Ідеальний баланс'",[5526,12869,5771],{"class":5536},[5526,12871,12872,12874,12876,12878,12880,12882,12884],{"class":5528,"line":7531},[5526,12873,6749],{"class":5536},[5526,12875,12459],{"class":5679},[5526,12877,5625],{"class":5536},[5526,12879,6342],{"class":5672},[5526,12881,5676],{"class":5536},[5526,12883,6761],{"class":5621},[5526,12885,5771],{"class":5536},[5526,12887,12888,12890,12893,12895,12897,12899,12901],{"class":5528,"line":7536},[5526,12889,6768],{"class":5536},[5526,12891,12892],{"class":5679},"'Train Acc - Test Acc (%)'",[5526,12894,5625],{"class":5536},[5526,12896,6342],{"class":5672},[5526,12898,5676],{"class":5536},[5526,12900,6761],{"class":5621},[5526,12902,5771],{"class":5536},[5526,12904,12905,12907,12910,12912,12914,12916,12918,12920,12922,12924,12926],{"class":5528,"line":7541},[5526,12906,6786],{"class":5536},[5526,12908,12909],{"class":5679},"'Overfitting Gap — Train vs Test'",[5526,12911,5625],{"class":5536},[5526,12913,6342],{"class":5672},[5526,12915,5676],{"class":5536},[5526,12917,6379],{"class":5621},[5526,12919,5625],{"class":5536},[5526,12921,6384],{"class":5672},[5526,12923,5676],{"class":5536},[5526,12925,6389],{"class":5679},[5526,12927,5771],{"class":5536},[5526,12929,12930,12932,12934,12936,12938],{"class":5528,"line":7547},[5526,12931,12516],{"class":5536},[5526,12933,6342],{"class":5672},[5526,12935,5676],{"class":5536},[5526,12937,5301],{"class":5621},[5526,12939,5771],{"class":5536},[5526,12941,12942,12944,12946,12948,12950],{"class":5528,"line":7569},[5526,12943,6817],{"class":5536},[5526,12945,6675],{"class":5672},[5526,12947,5676],{"class":5536},[5526,12949,6824],{"class":5621},[5526,12951,5771],{"class":5536},[5526,12953,12954],{"class":5528,"line":7584},[5526,12955,5593],{"emptyLinePlaceholder":5592},[5526,12957,12958],{"class":5528,"line":7589},[5526,12959,12960],{"class":5599},"# Графік 4: Фінальна таблиця результатів\n",[5526,12962,12963,12965,12967,12969,12971],{"class":5528,"line":7595},[5526,12964,12313],{"class":5536},[5526,12966,5514],{"class":5621},[5526,12968,5625],{"class":5536},[5526,12970,5514],{"class":5621},[5526,12972,6638],{"class":5536},[5526,12974,12975,12978,12980],{"class":5528,"line":7617},[5526,12976,12977],{"class":5536},"ax.axis(",[5526,12979,6356],{"class":5679},[5526,12981,5771],{"class":5536},[5526,12983,12984],{"class":5528,"line":7641},[5526,12985,5593],{"emptyLinePlaceholder":5592},[5526,12987,12988,12991,12994,12996,12999,13001,13004,13006,13009],{"class":5528,"line":7646},[5526,12989,12990],{"class":5536},"table_data = [[",[5526,12992,12993],{"class":5679},"'Модель'",[5526,12995,5625],{"class":5536},[5526,12997,12998],{"class":5679},"'Test Acc'",[5526,13000,5625],{"class":5536},[5526,13002,13003],{"class":5679},"'Покращення'",[5526,13005,5625],{"class":5536},[5526,13007,13008],{"class":5679},"'Train-Test Gap'",[5526,13010,13011],{"class":5536},"]]\n",[5526,13013,13014,13017],{"class":5528,"line":7662},[5526,13015,13016],{"class":5536},"baseline_acc_val = ",[5526,13018,13019],{"class":5621},"43.90\n",[5526,13021,13022],{"class":5528,"line":7670},[5526,13023,5593],{"emptyLinePlaceholder":5592},[5526,13025,13026,13028,13031,13033],{"class":5528,"line":7688},[5526,13027,5949],{"class":5532},[5526,13029,13030],{"class":5536}," name, (hist, best_acc) ",[5526,13032,5955],{"class":5532},[5526,13034,12333],{"class":5536},[5526,13036,13037],{"class":5528,"line":7711},[5526,13038,13039],{"class":5536},"    improvement = best_acc - baseline_acc_val\n",[5526,13041,13042,13045,13047,13050,13052,13055,13057,13059,13061],{"class":5528,"line":7734},[5526,13043,13044],{"class":5536},"    gap = hist[",[5526,13046,8027],{"class":5679},[5526,13048,13049],{"class":5536},"][-",[5526,13051,5514],{"class":5621},[5526,13053,13054],{"class":5536},"] - hist[",[5526,13056,8037],{"class":5679},[5526,13058,13049],{"class":5536},[5526,13060,5514],{"class":5621},[5526,13062,6638],{"class":5536},[5526,13064,13065],{"class":5528,"line":8538},[5526,13066,6251],{"class":5536},[5526,13068,13069,13072,13075,13077,13080,13083,13086],{"class":5528,"line":8568},[5526,13070,13071],{"class":5536},"    marker = ",[5526,13073,13074],{"class":5679},"\"🏆\"",[5526,13076,7556],{"class":5532},[5526,13078,13079],{"class":5536}," best_acc >= ",[5526,13081,13082],{"class":5621},"74",[5526,13084,13085],{"class":5532}," else",[5526,13087,13088],{"class":5679}," \"\"\n",[5526,13090,13091],{"class":5528,"line":8573},[5526,13092,13093],{"class":5536},"    table_data.append([\n",[5526,13095,13096],{"class":5528,"line":8579},[5526,13097,13098],{"class":5536},"        name,\n",[5526,13100,13101,13104,13106,13108,13110,13112,13115,13117,13120,13122,13124],{"class":5528,"line":8594},[5526,13102,13103],{"class":5690},"        f",[5526,13105,5768],{"class":5679},[5526,13107,5787],{"class":5690},[5526,13109,8621],{"class":5536},[5526,13111,7763],{"class":5690},[5526,13113,13114],{"class":5679},"% ",[5526,13116,5787],{"class":5690},[5526,13118,13119],{"class":5536},"marker",[5526,13121,5847],{"class":5690},[5526,13123,5768],{"class":5679},[5526,13125,8732],{"class":5536},[5526,13127,13128,13130,13133,13135,13138,13140,13142],{"class":5528,"line":8635},[5526,13129,13103],{"class":5690},[5526,13131,13132],{"class":5679},"\"+",[5526,13134,5787],{"class":5690},[5526,13136,13137],{"class":5536},"improvement",[5526,13139,7763],{"class":5690},[5526,13141,8563],{"class":5679},[5526,13143,8732],{"class":5536},[5526,13145,13146,13148,13150,13152,13155,13157],{"class":5528,"line":8640},[5526,13147,13103],{"class":5690},[5526,13149,5768],{"class":5679},[5526,13151,5787],{"class":5690},[5526,13153,13154],{"class":5536},"gap",[5526,13156,7763],{"class":5690},[5526,13158,13159],{"class":5679},"%\"\n",[5526,13161,13162],{"class":5528,"line":8649},[5526,13163,13164],{"class":5536},"    ])\n",[5526,13166,13167],{"class":5528,"line":8654},[5526,13168,5593],{"emptyLinePlaceholder":5592},[5526,13170,13171,13174,13177,13180,13183,13185,13188,13190,13192,13194,13197],{"class":5528,"line":8660},[5526,13172,13173],{"class":5536},"table = ax.table(",[5526,13175,13176],{"class":5672},"cellText",[5526,13178,13179],{"class":5536},"=table_data, ",[5526,13181,13182],{"class":5672},"cellLoc",[5526,13184,5676],{"class":5536},[5526,13186,13187],{"class":5679},"'left'",[5526,13189,5625],{"class":5536},[5526,13191,12527],{"class":5672},[5526,13193,5676],{"class":5536},[5526,13195,13196],{"class":5679},"'center'",[5526,13198,8732],{"class":5536},[5526,13200,13201,13204,13207,13210,13212,13215,13217,13220,13222,13225],{"class":5528,"line":8696},[5526,13202,13203],{"class":5672},"                colWidths",[5526,13205,13206],{"class":5536},"=[",[5526,13208,13209],{"class":5621},"0.40",[5526,13211,5625],{"class":5536},[5526,13213,13214],{"class":5621},"0.18",[5526,13216,5625],{"class":5536},[5526,13218,13219],{"class":5621},"0.22",[5526,13221,5625],{"class":5536},[5526,13223,13224],{"class":5621},"0.20",[5526,13226,5658],{"class":5536},[5526,13228,13229,13232,13234],{"class":5528,"line":8701},[5526,13230,13231],{"class":5536},"table.auto_set_font_size(",[5526,13233,5729],{"class":5690},[5526,13235,5771],{"class":5536},[5526,13237,13238,13241,13243],{"class":5528,"line":8707},[5526,13239,13240],{"class":5536},"table.set_fontsize(",[5526,13242,5301],{"class":5621},[5526,13244,5771],{"class":5536},[5526,13246,13247,13250,13252,13254,13256],{"class":5528,"line":8713},[5526,13248,13249],{"class":5536},"table.scale(",[5526,13251,5514],{"class":5621},[5526,13253,5625],{"class":5536},[5526,13255,12391],{"class":5621},[5526,13257,5771],{"class":5536},[5526,13259,13260],{"class":5528,"line":8735},[5526,13261,5593],{"emptyLinePlaceholder":5592},[5526,13263,13264],{"class":5528,"line":8744},[5526,13265,13266],{"class":5599},"# Стилізація заголовка\n",[5526,13268,13269,13271,13274,13276,13278,13280,13282],{"class":5528,"line":11440},[5526,13270,5949],{"class":5532},[5526,13272,13273],{"class":5536}," i ",[5526,13275,5955],{"class":5532},[5526,13277,8115],{"class":5754},[5526,13279,5758],{"class":5536},[5526,13281,6147],{"class":5621},[5526,13283,7021],{"class":5536},[5526,13285,13286,13289,13291,13294,13297],{"class":5528,"line":11450},[5526,13287,13288],{"class":5536},"    table[(",[5526,13290,5871],{"class":5621},[5526,13292,13293],{"class":5536},", i)].set_facecolor(",[5526,13295,13296],{"class":5679},"'#e2e8f0'",[5526,13298,5771],{"class":5536},[5526,13300,13301,13303,13305,13308,13311,13313,13315],{"class":5528,"line":11460},[5526,13302,13288],{"class":5536},[5526,13304,5871],{"class":5621},[5526,13306,13307],{"class":5536},", i)].set_text_props(",[5526,13309,13310],{"class":5672},"weight",[5526,13312,5676],{"class":5536},[5526,13314,6389],{"class":5679},[5526,13316,5771],{"class":5536},[5526,13318,13319],{"class":5528,"line":11465},[5526,13320,5593],{"emptyLinePlaceholder":5592},[5526,13322,13323],{"class":5528,"line":11475},[5526,13324,13325],{"class":5599},"# Виділення найкращого результату\n",[5526,13327,13328,13331,13333,13335,13337,13340,13343],{"class":5528,"line":11485},[5526,13329,13330],{"class":5536},"table[(",[5526,13332,6147],{"class":5621},[5526,13334,5625],{"class":5536},[5526,13336,5871],{"class":5621},[5526,13338,13339],{"class":5536},")].set_facecolor(",[5526,13341,13342],{"class":5679},"'#dcfce7'",[5526,13344,5771],{"class":5536},[5526,13346,13347,13349,13351,13353,13355,13357,13359],{"class":5528,"line":11495},[5526,13348,13330],{"class":5536},[5526,13350,6147],{"class":5621},[5526,13352,5625],{"class":5536},[5526,13354,5514],{"class":5621},[5526,13356,13339],{"class":5536},[5526,13358,13342],{"class":5679},[5526,13360,5771],{"class":5536},[5526,13362,13363,13365,13367,13369,13371,13373,13375],{"class":5528,"line":11500},[5526,13364,13330],{"class":5536},[5526,13366,6147],{"class":5621},[5526,13368,5625],{"class":5536},[5526,13370,6128],{"class":5621},[5526,13372,13339],{"class":5536},[5526,13374,13342],{"class":5679},[5526,13376,5771],{"class":5536},[5526,13378,13379,13381,13383,13385,13387,13389,13391],{"class":5528,"line":11507},[5526,13380,13330],{"class":5536},[5526,13382,6147],{"class":5621},[5526,13384,5625],{"class":5536},[5526,13386,6455],{"class":5621},[5526,13388,13339],{"class":5536},[5526,13390,13342],{"class":5679},[5526,13392,5771],{"class":5536},[5526,13394,13395],{"class":5528,"line":11512},[5526,13396,5593],{"emptyLinePlaceholder":5592},[5526,13398,13399],{"class":5528,"line":11517},[5526,13400,6396],{"class":5536},[5526,13402,13403,13406,13409,13411,13414,13416,13419,13421,13424,13426,13429],{"class":5528,"line":11531},[5526,13404,13405],{"class":5536},"plt.savefig(",[5526,13407,13408],{"class":5679},"'cifar100_final_comparison.png'",[5526,13410,5625],{"class":5536},[5526,13412,13413],{"class":5672},"dpi",[5526,13415,5676],{"class":5536},[5526,13417,13418],{"class":5621},"150",[5526,13420,5625],{"class":5536},[5526,13422,13423],{"class":5672},"bbox_inches",[5526,13425,5676],{"class":5536},[5526,13427,13428],{"class":5679},"'tight'",[5526,13430,5771],{"class":5536},[5526,13432,13433],{"class":5528,"line":11536},[5526,13434,6401],{"class":5536},[5526,13436,13437],{"class":5528,"line":11554},[5526,13438,5593],{"emptyLinePlaceholder":5592},[5526,13440,13441],{"class":5528,"line":11568},[5526,13442,13443],{"class":5599},"# Детальна таблиця у консолі\n",[5526,13445,13446,13448,13450,13452,13454,13456,13459,13461,13464,13467],{"class":5528,"line":11582},[5526,13447,5755],{"class":5754},[5526,13449,5758],{"class":5536},[5526,13451,5768],{"class":5679},[5526,13453,5765],{"class":5764},[5526,13455,5768],{"class":5679},[5526,13457,13458],{"class":5536}," + ",[5526,13460,8078],{"class":5679},[5526,13462,13463],{"class":5536},"*",[5526,13465,13466],{"class":5621},"90",[5526,13468,5771],{"class":5536},[5526,13470,13471,13473,13475,13478],{"class":5528,"line":11603},[5526,13472,5755],{"class":5754},[5526,13474,5758],{"class":5536},[5526,13476,13477],{"class":5679},"\"📊 ФІНАЛЬНІ РЕЗУЛЬТАТИ ПРОЄКТУ\"",[5526,13479,5771],{"class":5536},[5526,13481,13483,13485,13487,13489,13491,13493],{"class":5528,"line":13482},105,[5526,13484,5755],{"class":5754},[5526,13486,5758],{"class":5536},[5526,13488,8078],{"class":5679},[5526,13490,13463],{"class":5536},[5526,13492,13466],{"class":5621},[5526,13494,5771],{"class":5536},[5526,13496,13498,13500,13502,13504,13506,13508,13510,13513,13516,13518,13521,13523,13525,13528,13530,13533,13536,13538,13541,13543,13545],{"class":5528,"line":13497},106,[5526,13499,5755],{"class":5754},[5526,13501,5758],{"class":5536},[5526,13503,5781],{"class":5690},[5526,13505,5768],{"class":5679},[5526,13507,5787],{"class":5690},[5526,13509,12993],{"class":5679},[5526,13511,13512],{"class":5690},":\u003C35}",[5526,13514,13515],{"class":5690}," {",[5526,13517,12998],{"class":5679},[5526,13519,13520],{"class":5690},":\u003C12}",[5526,13522,13515],{"class":5690},[5526,13524,13003],{"class":5679},[5526,13526,13527],{"class":5690},":\u003C15}",[5526,13529,13515],{"class":5690},[5526,13531,13532],{"class":5679},"'Gap'",[5526,13534,13535],{"class":5690},":\u003C10}",[5526,13537,13515],{"class":5690},[5526,13539,13540],{"class":5679},"'Статус'",[5526,13542,5847],{"class":5690},[5526,13544,5768],{"class":5679},[5526,13546,5771],{"class":5536},[5526,13548,13550,13552,13554,13557,13559,13561],{"class":5528,"line":13549},107,[5526,13551,5755],{"class":5754},[5526,13553,5758],{"class":5536},[5526,13555,13556],{"class":5679},"\"-\"",[5526,13558,13463],{"class":5536},[5526,13560,13466],{"class":5621},[5526,13562,5771],{"class":5536},[5526,13564,13566],{"class":5528,"line":13565},108,[5526,13567,5593],{"emptyLinePlaceholder":5592},[5526,13569,13571,13573,13575,13577],{"class":5528,"line":13570},109,[5526,13572,5949],{"class":5532},[5526,13574,13030],{"class":5536},[5526,13576,5955],{"class":5532},[5526,13578,12333],{"class":5536},[5526,13580,13582],{"class":5528,"line":13581},110,[5526,13583,13039],{"class":5536},[5526,13585,13587,13589,13591,13593,13595,13597,13599,13601,13603],{"class":5528,"line":13586},111,[5526,13588,13044],{"class":5536},[5526,13590,8027],{"class":5679},[5526,13592,13049],{"class":5536},[5526,13594,5514],{"class":5621},[5526,13596,13054],{"class":5536},[5526,13598,8037],{"class":5679},[5526,13600,13049],{"class":5536},[5526,13602,5514],{"class":5621},[5526,13604,6638],{"class":5536},[5526,13606,13608],{"class":5528,"line":13607},112,[5526,13609,6251],{"class":5536},[5526,13611,13613,13616,13618,13620],{"class":5528,"line":13612},113,[5526,13614,13615],{"class":5532},"    if",[5526,13617,13079],{"class":5536},[5526,13619,12417],{"class":5621},[5526,13621,6879],{"class":5536},[5526,13623,13625,13628],{"class":5528,"line":13624},114,[5526,13626,13627],{"class":5536},"        status = ",[5526,13629,13630],{"class":5679},"\"✅ Ціль досягнута\"\n",[5526,13632,13634,13637],{"class":5528,"line":13633},115,[5526,13635,13636],{"class":5532},"    else",[5526,13638,6879],{"class":5536},[5526,13640,13642,13644],{"class":5528,"line":13641},116,[5526,13643,13627],{"class":5536},[5526,13645,13646],{"class":5679},"\"⏳ В процесі\"\n",[5526,13648,13650],{"class":5528,"line":13649},117,[5526,13651,6251],{"class":5536},[5526,13653,13655,13657,13659,13661,13663,13665,13668,13670,13672,13674,13677,13680,13682,13684,13687,13689,13691,13693,13696,13698,13700,13703,13705,13707],{"class":5528,"line":13654},118,[5526,13656,5973],{"class":5754},[5526,13658,5758],{"class":5536},[5526,13660,5781],{"class":5690},[5526,13662,5768],{"class":5679},[5526,13664,5787],{"class":5690},[5526,13666,13667],{"class":5536},"name",[5526,13669,13512],{"class":5690},[5526,13671,13515],{"class":5690},[5526,13673,8621],{"class":5536},[5526,13675,13676],{"class":5690},":\u003C12.2f}",[5526,13678,13679],{"class":5679},"% +",[5526,13681,5787],{"class":5690},[5526,13683,13137],{"class":5536},[5526,13685,13686],{"class":5690},":\u003C14.2f}",[5526,13688,13114],{"class":5679},[5526,13690,5787],{"class":5690},[5526,13692,13154],{"class":5536},[5526,13694,13695],{"class":5690},":\u003C10.2f}",[5526,13697,13114],{"class":5679},[5526,13699,5787],{"class":5690},[5526,13701,13702],{"class":5536},"status",[5526,13704,5847],{"class":5690},[5526,13706,5768],{"class":5679},[5526,13708,5771],{"class":5536},[5526,13710,13712],{"class":5528,"line":13711},119,[5526,13713,5593],{"emptyLinePlaceholder":5592},[5526,13715,13717,13719,13721,13723,13725,13727],{"class":5528,"line":13716},120,[5526,13718,5755],{"class":5754},[5526,13720,5758],{"class":5536},[5526,13722,8078],{"class":5679},[5526,13724,13463],{"class":5536},[5526,13726,13466],{"class":5621},[5526,13728,5771],{"class":5536},[5526,13730,13732,13734,13736,13738,13740,13742,13745],{"class":5528,"line":13731},121,[5526,13733,5755],{"class":5754},[5526,13735,5758],{"class":5536},[5526,13737,5781],{"class":5690},[5526,13739,5768],{"class":5679},[5526,13741,5765],{"class":5764},[5526,13743,13744],{"class":5679},"🎯 Цільова метрика: 72%\"",[5526,13746,5771],{"class":5536},[5526,13748,13750,13752,13754,13756,13759],{"class":5528,"line":13749},122,[5526,13751,5755],{"class":5754},[5526,13753,5758],{"class":5536},[5526,13755,5781],{"class":5690},[5526,13757,13758],{"class":5679},"\"🏆 Досягнутий результат: 74.05%\"",[5526,13760,5771],{"class":5536},[5526,13762,13764,13766,13768,13770,13773],{"class":5528,"line":13763},123,[5526,13765,5755],{"class":5754},[5526,13767,5758],{"class":5536},[5526,13769,5781],{"class":5690},[5526,13771,13772],{"class":5679},"\"✅ Перевищення цілі: +2.05%\"",[5526,13774,5771],{"class":5536},[5526,13776,13778,13780,13782,13784,13786,13788,13791],{"class":5528,"line":13777},124,[5526,13779,5755],{"class":5754},[5526,13781,5758],{"class":5536},[5526,13783,5781],{"class":5690},[5526,13785,5768],{"class":5679},[5526,13787,5765],{"class":5764},[5526,13789,13790],{"class":5679},"💡 Загальне покращення від baseline: +30.15% (43.90% → 74.05%)\"",[5526,13792,5771],{"class":5536},[6016,13794,13795,13802,13809,13905,13919],{"v-slot:output":5522},[5191,13796,13797],{},[6405,13798],{"alt":13799,"className":13800,"src":13801},"Фінальне порівняння всіх моделей — прогрес від baseline до ResNet",[6409],"\u002Fimages\u002Fai-python\u002Fbatching-normalization-deep-networks\u002Ffinal-project\u002F03.png",[8750,13803,13805,13806],{"id":13804},"фінальні-результати-проєкту","================================================================================\n📊 ",[5213,13807,13808],{},"ФІНАЛЬНІ РЕЗУЛЬТАТИ ПРОЄКТУ",[5270,13810,13811,13830],{},[5273,13812,13813],{},[5276,13814,13815,13818,13821,13824,13827],{},[5279,13816,13817],{"align":5281},"Модель",[5279,13819,13820],{"align":5281},"Test Acc",[5279,13822,13823],{"align":5281},"Покращення",[5279,13825,13826],{"align":5281},"Gap",[5279,13828,13829],{"align":5281},"Статус",[5289,13831,13832,13848,13862,13876,13892],{},[5276,13833,13834,13837,13839,13842,13845],{},[5294,13835,13836],{"align":5281},"Baseline (No opt)",[5294,13838,8766],{"align":5281},[5294,13840,13841],{"align":5281},"+0.00%",[5294,13843,13844],{"align":5281},"32.44%",[5294,13846,13847],{"align":5281},"⏳ В процесі",[5276,13849,13850,13852,13854,13857,13860],{},[5294,13851,5455],{"align":5281},[5294,13853,9391],{"align":5281},[5294,13855,13856],{"align":5281},"+10.90%",[5294,13858,13859],{"align":5281},"3.87%",[5294,13861,13847],{"align":5281},[5276,13863,13864,13866,13868,13871,13874],{},[5294,13865,5461],{"align":5281},[5294,13867,10397],{"align":5281},[5294,13869,13870],{"align":5281},"+23.00%",[5294,13872,13873],{"align":5281},"11.44%",[5294,13875,13847],{"align":5281},[5276,13877,13878,13881,13883,13886,13889],{},[5294,13879,13880],{"align":5281},"+ ResNet + Scheduling",[5294,13882,12060],{"align":5281},[5294,13884,13885],{"align":5281},"+30.15%",[5294,13887,13888],{"align":5281},"11.62%",[5294,13890,13891],{"align":5281},"✅ Ціль досягнута 🏆",[5276,13893,13894,13897,13899,13901,13903],{},[5294,13895,13896],{"align":5281},"================================================================================",[5294,13898],{"align":5281},[5294,13900],{"align":5281},[5294,13902],{"align":5281},[5294,13904],{"align":5281},[5191,13906,13907,13908,13910,13911,13914,13915,13918],{},"🎯 ",[5213,13909,5415],{}," 72%\n🏆 ",[5213,13912,13913],{},"Досягнутий результат:"," 74.05%\n✅ ",[5213,13916,13917],{},"Перевищення цілі:"," +2.05%",[5191,13920,13921,13922,13925],{},"💡 ",[5213,13923,13924],{},"Загальне покращення від baseline:"," +30.15% (43.90% → 74.05%)",[5191,13927,7798],{},[6929,13929,13930,13935],{},[5191,13931,13932],{},[5213,13933,13934],{},"Інтерпретація графіків:",[5443,13936,13937,13943,13949,13955],{},[5381,13938,13939,13942],{},[5213,13940,13941],{},"Test Accuracy (верхній лівий):"," Чітко видно еволюцію — кожна техніка додає покращення. ResNet (фіолетова лінія) перетинає червону пунктирну (72%) на епосі ~75.",[5381,13944,13945,13948],{},[5213,13946,13947],{},"Test Loss (верхній правий):"," Loss поступово зменшується з кожною ітерацією. Baseline застряг на ~2.4, ResNet досяг 0.88.",[5381,13950,13951,13954],{},[5213,13952,13953],{},"Overfitting Gap (нижній лівий):"," Baseline має величезний gap (32%), що зменшується з augmentation (4%), але трохи зростає з BatchNorm\u002FResNet (11-12%) через їхню велику ємність. Це нормально — важливо, щоб test accuracy зростала!",[5381,13956,13957,13960],{},[5213,13958,13959],{},"Фінальна таблиця:"," Резюме покращень. Кожен крок давав +8-12% accuracy!",[5483,13962],{},[5186,13964,13966],{"id":13965},"ключові-висновки-проєкту","Ключові висновки проєкту",[5191,13968,13969],{},"Підсумуємо, що ми опанували через цей комплексний проєкт:",[5253,13971,13973],{"id":13972},"вклад-кожної-техніки","Вклад кожної техніки",[5195,13975,13976,13995,14009],{},[5198,13977,13980,13986,13989],{"icon":13978,"title":13979},"i-lucide-image","1️⃣ Data Augmentation: +10.9%",[5191,13981,13982,13985],{},[5213,13983,13984],{},"Найбільший single стрибок"," у точності!",[5191,13987,13988],{},"Випадкові трансформації (crop, flip, color jitter) штучно збільшують датасет та навчають модель інваріантності. Це критично для малих датасетів (500 прикладів\u002Fклас).",[5191,13990,13991,13994],{},[5213,13992,13993],{},"Коли використовувати:"," Завжди! Це перше, що треба додати після baseline.",[5198,13996,13998,14001,14004],{"icon":4660,"title":13997},"2️⃣ BatchNorm + Dropout: +12.1%",[5191,13999,14000],{},"BatchNorm стабілізує навчання, дозволяє великі LR. Dropout регуляризує.",[5191,14002,14003],{},"Комбінація двох технік дала синергетичний ефект. Зверніть увагу: ми також збільшили кількість каналів (64→128→256→512), що додало ємності.",[5191,14005,14006,14008],{},[5213,14007,13993],{}," BN — завжди після Conv. Dropout — опційно, залежно від архітектури (ResNet часто без Dropout).",[5198,14010,14012,14015,14018],{"icon":943,"title":14011},"3️⃣ ResNet + Scheduling: +7.15%",[5191,14013,14014],{},"Skip Connections дозволили навчати 18 шарів замість 4. Warm-up + Cosine Annealing оптимізували конвергенцію.",[5191,14016,14017],{},"Глибша архітектура + оптимальний scheduler — фінальний штрих до state-of-the-art результату.",[5191,14019,14020,14022],{},[5213,14021,13993],{}," Для будь-яких глибоких мереж (>10 шарів). Scheduler — завжди!",[5253,14024,14026],{"id":14025},"універсальна-стратегія-побудови-класифікатора","Універсальна стратегія побудови класифікатора",[5191,14028,14029],{},"Ця послідовність працює для більшості задач комп'ютерного зору:",[14031,14032,14033,14037,14040,14046,14050,14053,14059,14063,14074,14079,14083,14086,14091,14095,14109],"steps",{},[5253,14034,14036],{"id":14035},"крок-1-baseline","Крок 1. Baseline",[5191,14038,14039],{},"Побудуйте максимально просту модель без оптимізацій. Це дасть відправну точку для порівняння.",[5191,14041,14042,14045],{},[5213,14043,14044],{},"Час:"," 1-2 години (включно з аналізом даних)",[5253,14047,14049],{"id":14048},"крок-2-data-augmentation","Крок 2. Data Augmentation",[5191,14051,14052],{},"Додайте випадкові трансформації до тренувальних даних. Це завжди перше покращення.",[5191,14054,14055,14058],{},[5213,14056,14057],{},"Очікування:"," +10-15% accuracy",[5253,14060,14062],{"id":14061},"крок-3-архітектурні-покращення","Крок 3. Архітектурні покращення",[5378,14064,14065,14068,14071],{},[5381,14066,14067],{},"Додайте BatchNorm після кожного Conv",[5381,14069,14070],{},"Додайте Dropout перед FC (опційно)",[5381,14072,14073],{},"Збільшіть кількість каналів\u002Fшарів",[5191,14075,14076,14078],{},[5213,14077,14057],{}," +5-10% accuracy",[5253,14080,14082],{"id":14081},"крок-4-використайте-сучасну-архітектуру","Крок 4. Використайте сучасну архітектуру",[5191,14084,14085],{},"Замініть власну CNN на ResNet\u002FEfficientNet\u002FConvNeXt (або реалізуйте residual blocks).",[5191,14087,14088,14090],{},[5213,14089,14057],{}," +5-8% accuracy",[5253,14092,14094],{"id":14093},"крок-5-оптимізуйте-гіперпараметри","Крок 5. Оптимізуйте гіперпараметри",[5378,14096,14097,14100,14103,14106],{},[5381,14098,14099],{},"Налаштуйте LR Scheduler (Warm-up + Cosine)",[5381,14101,14102],{},"Підберіть optimal batch size",[5381,14104,14105],{},"Експериментуйте з weight decay",[5381,14107,14108],{},"Тренуйте довше (100-200 епох)",[5191,14110,14111,14113],{},[5213,14112,14057],{}," +2-5% accuracy",[6412,14115,14116,14121,14135],{},[5191,14117,14118],{},[5213,14119,14120],{},"Загальне правило:",[5191,14122,14123,14124,5625,14127,14130,14131,14134],{},"90% успіху — це ",[5213,14125,14126],{},"Data Augmentation",[5213,14128,14129],{},"BatchNorm"," та ",[5213,14132,14133],{},"правильна архітектура"," (ResNet). Решта 10% — fine-tuning гіперпараметрів.",[5191,14136,14137],{},"Не витрачайте тижні на підбір ідеального learning rate, поки не застосували базові техніки!",[5483,14139],{},[5186,14141,14143],{"id":14142},"практичні-завдання-для-самостійної-роботи","Практичні завдання для самостійної роботи",[5191,14145,14146],{},"Застосуйте вивчені техніки до нових задач:",[14148,14149,14150,14213,14328,14514],"accordion",{},[14151,14152,14155,14161,14166,14207],"accordion-item",{"icon":14153,"label":14154},"i-lucide-clipboard-list","📋 Завдання 1: Покращити фінальну модель до 75%+",[5191,14156,14157,14160],{},[5213,14158,14159],{},"Мета:"," Досягти Test Accuracy > 75% на CIFAR-100",[5191,14162,14163],{},[5213,14164,14165],{},"Підказки:",[5443,14167,14168,14183,14189,14195,14201],{},[5381,14169,14170,14173,14174,5625,14177,5625,14180],{},[5213,14171,14172],{},"Більше augmentations:"," Додайте ",[5203,14175,14176],{},"RandomRotation",[5203,14178,14179],{},"RandomAffine",[5203,14181,14182],{},"RandomErasing",[5381,14184,14185,14188],{},[5213,14186,14187],{},"MixUp\u002FCutMix:"," Advanced техніки data augmentation",[5381,14190,14191,14194],{},[5213,14192,14193],{},"Глибша ResNet:"," Спробуйте ResNet-34 або ResNet-50",[5381,14196,14197,14200],{},[5213,14198,14199],{},"Test-Time Augmentation (TTA):"," Усередніть predictions по кількох augmented versions тестового зображення",[5381,14202,14203,14206],{},[5213,14204,14205],{},"Label Smoothing:"," Замість one-hot labels використовуйте soft labels (0.9 замість 1.0)",[5191,14208,14209,14212],{},[5213,14210,14211],{},"Очікуваний результат:"," 75-77% accuracy",[14151,14214,14217,14222,14227,14238,14243,14248],{"icon":14215,"label":14216},"i-lucide-shirt","📋 Завдання 2: Застосувати до Fashion-MNIST",[5191,14218,14219,14221],{},[5213,14220,14159],{}," Побудувати classifier для Fashion-MNIST (28×28 grayscale, 10 класів)",[5191,14223,14224],{},[5213,14225,14226],{},"Відмінності від CIFAR-100:",[5378,14228,14229,14232,14235],{},[5381,14230,14231],{},"Grayscale (1 канал) замість RGB (3)",[5381,14233,14234],{},"Менша роздільність (28×28 vs 32×32)",[5381,14236,14237],{},"Легша задача (10 класів vs 100)",[5191,14239,14240,14242],{},[5213,14241,14211],{}," >94% accuracy (human-level ~95%)",[5191,14244,14245],{},[5213,14246,14247],{},"Адаптації:",[5517,14249,14251],{"className":5519,"code":14250,"language":5521,"meta":5522,"style":5522},"# Змінити перший Conv\nself.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1)  # 1 канал\n\n# Менше augmentations (Fashion-MNIST простіший)\ntransforms.RandomCrop(28, padding=4),\ntransforms.RandomHorizontalFlip()\n# Без ColorJitter (grayscale!)\n",[5203,14252,14253,14258,14291,14295,14300,14318,14323],{"__ignoreMap":5522},[5526,14254,14255],{"class":5528,"line":5529},[5526,14256,14257],{"class":5599},"# Змінити перший Conv\n",[5526,14259,14260,14262,14264,14266,14268,14270,14272,14274,14276,14278,14280,14282,14284,14286,14288],{"class":5528,"line":5540},[5526,14261,7082],{"class":5690},[5526,14263,7130],{"class":5536},[5526,14265,5514],{"class":5621},[5526,14267,5625],{"class":5536},[5526,14269,7137],{"class":5621},[5526,14271,5625],{"class":5536},[5526,14273,7142],{"class":5672},[5526,14275,5676],{"class":5536},[5526,14277,6455],{"class":5621},[5526,14279,5625],{"class":5536},[5526,14281,7151],{"class":5672},[5526,14283,5676],{"class":5536},[5526,14285,5514],{"class":5621},[5526,14287,7173],{"class":5536},[5526,14289,14290],{"class":5599},"# 1 канал\n",[5526,14292,14293],{"class":5528,"line":5548},[5526,14294,5593],{"emptyLinePlaceholder":5592},[5526,14296,14297],{"class":5528,"line":5562},[5526,14298,14299],{"class":5599},"# Менше augmentations (Fashion-MNIST простіший)\n",[5526,14301,14302,14305,14308,14310,14312,14314,14316],{"class":5528,"line":5576},[5526,14303,14304],{"class":5536},"transforms.RandomCrop(",[5526,14306,14307],{"class":5621},"28",[5526,14309,5625],{"class":5536},[5526,14311,7151],{"class":5672},[5526,14313,5676],{"class":5536},[5526,14315,6147],{"class":5621},[5526,14317,11209],{"class":5536},[5526,14319,14320],{"class":5528,"line":5589},[5526,14321,14322],{"class":5536},"transforms.RandomHorizontalFlip()\n",[5526,14324,14325],{"class":5528,"line":5596},[5526,14326,14327],{"class":5599},"# Без ColorJitter (grayscale!)\n",[14151,14329,14331,14336,14341,14352,14357,14362,14392,14397,14466,14471,14485,14490,14509],{"icon":13978,"label":14330},"📋 Завдання 3: Transfer Learning на власному датасеті",[5191,14332,14333,14335],{},[5213,14334,14159],{}," Fine-tune pretrained ResNet-18 на власному малому датасеті (\u003C1000 зображень)",[5191,14337,14338],{},[5213,14339,14340],{},"Приклад датасетів:",[5378,14342,14343,14346,14349],{},[5381,14344,14345],{},"Oxford-IIIT Pets (37 пород собак\u002Fкотів)",[5381,14347,14348],{},"Caltech-101 (101 категорія об'єктів)",[5381,14350,14351],{},"Або власний датасет!",[5191,14353,14354],{},[5213,14355,14356],{},"Підхід:",[5443,14358,14359],{},[5381,14360,14361],{},"Завантажте pretrained ResNet-18 (на ImageNet):",[5517,14363,14365],{"className":5519,"code":14364,"language":5521,"meta":5522,"style":5522},"from torchvision import models\nmodel = models.resnet18(pretrained=True)\n",[5203,14366,14367,14378],{"__ignoreMap":5522},[5526,14368,14369,14371,14373,14375],{"class":5528,"line":5529},[5526,14370,5551],{"class":5532},[5526,14372,5554],{"class":5536},[5526,14374,5533],{"class":5532},[5526,14376,14377],{"class":5536}," models\n",[5526,14379,14380,14383,14386,14388,14390],{"class":5528,"line":5540},[5526,14381,14382],{"class":5536},"model = models.resnet18(",[5526,14384,14385],{"class":5672},"pretrained",[5526,14387,5676],{"class":5536},[5526,14389,5691],{"class":5690},[5526,14391,5771],{"class":5536},[5443,14393,14394],{"start":5540},[5381,14395,14396],{},"Заморозьте ранні шари:",[5517,14398,14400],{"className":5519,"code":14399,"language":5521,"meta":5522,"style":5522},"for param in model.parameters():\n    param.requires_grad = False\n\n# Розморозьте останні 2 блоки\nfor param in model.layer3.parameters():\n    param.requires_grad = True\nfor param in model.layer4.parameters():\n    param.requires_grad = True\n",[5203,14401,14402,14414,14422,14426,14431,14442,14449,14460],{"__ignoreMap":5522},[5526,14403,14404,14406,14409,14411],{"class":5528,"line":5529},[5526,14405,5949],{"class":5532},[5526,14407,14408],{"class":5536}," param ",[5526,14410,5955],{"class":5532},[5526,14412,14413],{"class":5536}," model.parameters():\n",[5526,14415,14416,14419],{"class":5528,"line":5540},[5526,14417,14418],{"class":5536},"    param.requires_grad = ",[5526,14420,14421],{"class":5690},"False\n",[5526,14423,14424],{"class":5528,"line":5548},[5526,14425,5593],{"emptyLinePlaceholder":5592},[5526,14427,14428],{"class":5528,"line":5562},[5526,14429,14430],{"class":5599},"# Розморозьте останні 2 блоки\n",[5526,14432,14433,14435,14437,14439],{"class":5528,"line":5576},[5526,14434,5949],{"class":5532},[5526,14436,14408],{"class":5536},[5526,14438,5955],{"class":5532},[5526,14440,14441],{"class":5536}," model.layer3.parameters():\n",[5526,14443,14444,14446],{"class":5528,"line":5589},[5526,14445,14418],{"class":5536},[5526,14447,14448],{"class":5690},"True\n",[5526,14450,14451,14453,14455,14457],{"class":5528,"line":5596},[5526,14452,5949],{"class":5532},[5526,14454,14408],{"class":5536},[5526,14456,5955],{"class":5532},[5526,14458,14459],{"class":5536}," model.layer4.parameters():\n",[5526,14461,14462,14464],{"class":5528,"line":5603},[5526,14463,14418],{"class":5536},[5526,14465,14448],{"class":5690},[5443,14467,14468],{"start":5548},[5381,14469,14470],{},"Замініть останній FC:",[5517,14472,14474],{"className":5519,"code":14473,"language":5521,"meta":5522,"style":5522},"model.fc = nn.Linear(512, num_classes)\n",[5203,14475,14476],{"__ignoreMap":5522},[5526,14477,14478,14481,14483],{"class":5528,"line":5529},[5526,14479,14480],{"class":5536},"model.fc = nn.Linear(",[5526,14482,7328],{"class":5621},[5526,14484,7343],{"class":5536},[5443,14486,14487],{"start":5562},[5381,14488,14489],{},"Тренуйте з малим LR (1e-4):",[5517,14491,14493],{"className":5519,"code":14492,"language":5521,"meta":5522,"style":5522},"optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",[5203,14494,14495],{"__ignoreMap":5522},[5526,14496,14497,14500,14502,14504,14507],{"class":5528,"line":5529},[5526,14498,14499],{"class":5536},"optimizer = optim.Adam(model.parameters(), ",[5526,14501,8666],{"class":5672},[5526,14503,5676],{"class":5536},[5526,14505,14506],{"class":5621},"1e-4",[5526,14508,5771],{"class":5536},[5191,14510,14511,14513],{},[5213,14512,14211],{}," Навіть на 500 зображеннях досягнете >85% accuracy завдяки pretrained features!",[14151,14515,14517,14522,14526,14545,14641,14646],{"icon":943,"label":14516},"📋 Завдання 4: Побудувати Ensemble",[5191,14518,14519,14521],{},[5213,14520,14159],{}," Об'єднати кілька моделей для підвищення accuracy",[5191,14523,14524],{},[5213,14525,14356],{},[5443,14527,14528,14542],{},[5381,14529,14530,14531],{},"Натренуйте 3-5 моделей з різними:",[5378,14532,14533,14536,14539],{},[5381,14534,14535],{},"Ініціалізаціями (різний random seed)",[5381,14537,14538],{},"Архітектурами (ResNet-18, ResNet-34, Wide ResNet)",[5381,14540,14541],{},"Augmentations",[5381,14543,14544],{},"Зберіть їхні передбачення:",[5517,14546,14548],{"className":5519,"code":14547,"language":5521,"meta":5522,"style":5522},"# Inference для кожної моделі\npreds_model1 = model1(x)\npreds_model2 = model2(x)\npreds_model3 = model3(x)\n\n# Усереднення softmax probabilities\nensemble_preds = (F.softmax(preds_model1, dim=1) + \n                 F.softmax(preds_model2, dim=1) + \n                 F.softmax(preds_model3, dim=1)) \u002F 3\n\nfinal_class = ensemble_preds.argmax(dim=1)\n",[5203,14549,14550,14555,14560,14565,14570,14574,14579,14594,14607,14624,14628],{"__ignoreMap":5522},[5526,14551,14552],{"class":5528,"line":5529},[5526,14553,14554],{"class":5599},"# Inference для кожної моделі\n",[5526,14556,14557],{"class":5528,"line":5540},[5526,14558,14559],{"class":5536},"preds_model1 = model1(x)\n",[5526,14561,14562],{"class":5528,"line":5548},[5526,14563,14564],{"class":5536},"preds_model2 = model2(x)\n",[5526,14566,14567],{"class":5528,"line":5562},[5526,14568,14569],{"class":5536},"preds_model3 = model3(x)\n",[5526,14571,14572],{"class":5528,"line":5576},[5526,14573,5593],{"emptyLinePlaceholder":5592},[5526,14575,14576],{"class":5528,"line":5589},[5526,14577,14578],{"class":5599},"# Усереднення softmax probabilities\n",[5526,14580,14581,14584,14587,14589,14591],{"class":5528,"line":5596},[5526,14582,14583],{"class":5536},"ensemble_preds = (F.softmax(preds_model1, ",[5526,14585,14586],{"class":5672},"dim",[5526,14588,5676],{"class":5536},[5526,14590,5514],{"class":5621},[5526,14592,14593],{"class":5536},") + \n",[5526,14595,14596,14599,14601,14603,14605],{"class":5528,"line":5603},[5526,14597,14598],{"class":5536},"                 F.softmax(preds_model2, ",[5526,14600,14586],{"class":5672},[5526,14602,5676],{"class":5536},[5526,14604,5514],{"class":5621},[5526,14606,14593],{"class":5536},[5526,14608,14609,14612,14614,14616,14618,14621],{"class":5528,"line":5609},[5526,14610,14611],{"class":5536},"                 F.softmax(preds_model3, ",[5526,14613,14586],{"class":5672},[5526,14615,5676],{"class":5536},[5526,14617,5514],{"class":5621},[5526,14619,14620],{"class":5536},")) \u002F ",[5526,14622,14623],{"class":5621},"3\n",[5526,14625,14626],{"class":5528,"line":5615},[5526,14627,5593],{"emptyLinePlaceholder":5592},[5526,14629,14630,14633,14635,14637,14639],{"class":5528,"line":5655},[5526,14631,14632],{"class":5536},"final_class = ensemble_preds.argmax(",[5526,14634,14586],{"class":5672},[5526,14636,5676],{"class":5536},[5526,14638,5514],{"class":5621},[5526,14640,5771],{"class":5536},[5191,14642,14643,14645],{},[5213,14644,14211],{}," +1-3% accuracy порівняно з single model",[5191,14647,14648,14651],{},[5213,14649,14650],{},"Недолік:"," Потребує 3-5× більше часу inference та пам'яті",[5483,14653],{},[5186,14655,14657],{"id":14656},"рефлексія-та-наступні-кроки","Рефлексія та наступні кроки",[5253,14659,14661],{"id":14660},"що-ми-досягли","Що ми досягли",[5191,14663,14664],{},"Через цей модуль ми пройшли повний шлях від теоретичних основ до production-ready моделі:",[5195,14666,14667,14685,14703],{},[5198,14668,14671],{"icon":14669,"title":14670},"i-lucide-book","📚 Теорія",[5378,14672,14673,14676,14679,14682],{},[5381,14674,14675],{},"Розуміння математики градієнтного спуску",[5381,14677,14678],{},"Знання проблем vanishing\u002Fexploding gradients",[5381,14680,14681],{},"Інтуїція за degradation problem та residual learning",[5381,14683,14684],{},"Розуміння internal covariate shift та BatchNorm",[5198,14686,14689],{"icon":14687,"title":14688},"i-lucide-code","💻 Практика",[5378,14690,14691,14694,14697,14700],{},[5381,14692,14693],{},"Реалізація ResNet з нуля",[5381,14695,14696],{},"Імплементація schedulers (Warm-up, Cosine)",[5381,14698,14699],{},"Робота з PyTorch DataLoader та трансформаціями",[5381,14701,14702],{},"Тренування глибоких мереж на GPU",[5198,14704,14707],{"icon":14705,"title":14706},"i-lucide-flask","🔬 Експерименти",[5378,14708,14709,14712,14715,14718],{},[5381,14710,14711],{},"Контрольовані порівняння різних технік",[5381,14713,14714],{},"Систематичний підхід до покращення моделі",[5381,14716,14717],{},"Візуалізація та інтерпретація результатів",[5381,14719,14720],{},"Досягнення state-of-the-art accuracy (74% на CIFAR-100)",[5253,14722,14724],{"id":14723},"куди-рухатись-далі","Куди рухатись далі",[5195,14726,14727,14756,14785],{},[5198,14728,14730],{"icon":4749,"title":14729},"🚀 Advanced архітектури",[5378,14731,14732,14738,14744,14750],{},[5381,14733,14734,14737],{},[5213,14735,14736],{},"Vision Transformers (ViT):"," Революція у CV через self-attention",[5381,14739,14740,14743],{},[5213,14741,14742],{},"EfficientNet:"," Compound scaling для оптимальної архітектури",[5381,14745,14746,14749],{},[5213,14747,14748],{},"ConvNeXt:"," Модернізовані CNN, що конкурують з Transformers",[5381,14751,14752,14755],{},[5213,14753,14754],{},"CLIP, DINO:"," Self-supervised learning та multimodal models",[5198,14757,14759],{"icon":5370,"title":14758},"🎯 Specialized задачі",[5378,14760,14761,14767,14773,14779],{},[5381,14762,14763,14766],{},[5213,14764,14765],{},"Object Detection:"," YOLO, Faster R-CNN, DETR",[5381,14768,14769,14772],{},[5213,14770,14771],{},"Semantic Segmentation:"," U-Net, DeepLab, Mask R-CNN",[5381,14774,14775,14778],{},[5213,14776,14777],{},"Generative Models:"," GANs, Diffusion Models, VAEs",[5381,14780,14781,14784],{},[5213,14782,14783],{},"Video Understanding:"," 3D CNN, SlowFast Networks",[5198,14786,14789],{"icon":14787,"title":14788},"i-lucide-factory","🏭 Production ML",[5378,14790,14791,14797,14803,14809],{},[5381,14792,14793,14796],{},[5213,14794,14795],{},"Model Optimization:"," Quantization, Pruning, Distillation",[5381,14798,14799,14802],{},[5213,14800,14801],{},"Deployment:"," ONNX, TorchScript, TensorRT",[5381,14804,14805,14808],{},[5213,14806,14807],{},"MLOps:"," Experiment tracking (W&B, MLflow), CI\u002FCD for ML",[5381,14810,14811,14814],{},[5213,14812,14813],{},"Monitoring:"," Data drift detection, model performance tracking",[6929,14816,14817,14822,14854],{},[5191,14818,14819],{},[5213,14820,14821],{},"Рекомендовані ресурси:",[5443,14823,14824,14830,14836,14842,14848],{},[5381,14825,14826,14829],{},[5213,14827,14828],{},"Papers With Code"," — бенчмарки та код state-of-the-art моделей",[5381,14831,14832,14835],{},[5213,14833,14834],{},"PyTorch Tutorials"," — офіційна документація з прикладами",[5381,14837,14838,14841],{},[5213,14839,14840],{},"fast.ai Course"," — практичний підхід до глибокого навчання",[5381,14843,14844,14847],{},[5213,14845,14846],{},"CS231n Stanford"," — фундаментальний курс з Computer Vision",[5381,14849,14850,14853],{},[5213,14851,14852],{},"Kaggle Competitions"," — практика на реальних задачах з призовим фондом",[5191,14855,14856,14859],{},[5213,14857,14858],{},"Найкращий спосіб навчатися — робити проєкти!"," Оберіть задачу, що вас цікавить, та застосуйте вивчені техніки.",[5483,14861],{},[5186,14863,14865],{"id":14864},"фінальний-чек-лист-успішного-ml-проєкту","Фінальний чек-лист успішного ML проєкту",[5191,14867,14868],{},"Використовуйте цей чек-лист для кожного нового проєкту комп'ютерного зору:",[14031,14870,14871,14875,14906,14910,14931,14935,14956,14960,14987,14991,15018,15022,15049,15053],{},[5253,14872,14874],{"id":14873},"крок-1-розуміння-даних","Крок 1. Розуміння даних",[5378,14876,14879,14888,14894,14900],{"className":14877},[14878],"contains-task-list",[5381,14880,14883,14887],{"className":14881},[14882],"task-list-item",[14884,14885],"input",{"disabled":5592,"type":14886},"checkbox"," Проаналізувати розподіл класів (баланс?)",[5381,14889,14891,14893],{"className":14890},[14882],[14884,14892],{"disabled":5592,"type":14886}," Перевірити якість зображень (роздільність, шум)",[5381,14895,14897,14899],{"className":14896},[14882],[14884,14898],{"disabled":5592,"type":14886}," Візуалізувати приклади з кожного класу",[5381,14901,14903,14905],{"className":14902},[14882],[14884,14904],{"disabled":5592,"type":14886}," Оцінити складність задачі (human baseline?)",[5253,14907,14909],{"id":14908},"крок-2-baseline-модель","Крок 2. Baseline модель",[5378,14911,14913,14919,14925],{"className":14912},[14878],[5381,14914,14916,14918],{"className":14915},[14882],[14884,14917],{"disabled":5592,"type":14886}," Побудувати просту CNN без оптимізацій",[5381,14920,14922,14924],{"className":14921},[14882],[14884,14923],{"disabled":5592,"type":14886}," Виміряти baseline accuracy",[5381,14926,14928,14930],{"className":14927},[14882],[14884,14929],{"disabled":5592,"type":14886}," Переконатись, що модель краща за random guess",[5253,14932,14934],{"id":14933},"крок-3-data-augmentation","Крок 3. Data Augmentation",[5378,14936,14938,14944,14950],{"className":14937},[14878],[5381,14939,14941,14943],{"className":14940},[14882],[14884,14942],{"disabled":5592,"type":14886}," Додати RandomCrop, HorizontalFlip",[5381,14945,14947,14949],{"className":14946},[14882],[14884,14948],{"disabled":5592,"type":14886}," Експериментувати з ColorJitter",[5381,14951,14953,14955],{"className":14952},[14882],[14884,14954],{"disabled":5592,"type":14886}," Перевірити, чи augmentations семантично правильні",[5253,14957,14959],{"id":14958},"крок-4-архітектура","Крок 4. Архітектура",[5378,14961,14963,14969,14975,14981],{"className":14962},[14878],[5381,14964,14966,14968],{"className":14965},[14882],[14884,14967],{"disabled":5592,"type":14886}," Додати BatchNorm після кожного Conv",[5381,14970,14972,14974],{"className":14971},[14882],[14884,14973],{"disabled":5592,"type":14886}," Розглянути Dropout перед FC",[5381,14976,14978,14980],{"className":14977},[14882],[14884,14979],{"disabled":5592,"type":14886}," Використати ResNet або іншу сучасну архітектуру",[5381,14982,14984,14986],{"className":14983},[14882],[14884,14985],{"disabled":5592,"type":14886}," Застосувати He initialization",[5253,14988,14990],{"id":14989},"крок-5-оптимізація","Крок 5. Оптимізація",[5378,14992,14994,15000,15006,15012],{"className":14993},[14878],[5381,14995,14997,14999],{"className":14996},[14882],[14884,14998],{"disabled":5592,"type":14886}," Обрати optimizer (SGD з momentum або Adam)",[5381,15001,15003,15005],{"className":15002},[14882],[14884,15004],{"disabled":5592,"type":14886}," Налаштувати LR Scheduler (Cosine + Warm-up)",[5381,15007,15009,15011],{"className":15008},[14882],[14884,15010],{"disabled":5592,"type":14886}," Додати Weight Decay (5e-4 як стандарт)",[5381,15013,15015,15017],{"className":15014},[14882],[14884,15016],{"disabled":5592,"type":14886}," Тренувати достатньо довго (100+ епох)",[5253,15019,15021],{"id":15020},"крок-6-моніторинг","Крок 6. Моніторинг",[5378,15023,15025,15031,15037,15043],{"className":15024},[14878],[5381,15026,15028,15030],{"className":15027},[14882],[14884,15029],{"disabled":5592,"type":14886}," Відстежувати train\u002Fval loss та accuracy",[5381,15032,15034,15036],{"className":15033},[14882],[14884,15035],{"disabled":5592,"type":14886}," Перевіряти train-test gap (overfitting?)",[5381,15038,15040,15042],{"className":15039},[14882],[14884,15041],{"disabled":5592,"type":14886}," Зберігати найкращу модель (за val accuracy)",[5381,15044,15046,15048],{"className":15045},[14882],[14884,15047],{"disabled":5592,"type":14886}," Візуалізувати помилки моделі",[5253,15050,15052],{"id":15051},"крок-7-фінальна-оцінка","Крок 7. Фінальна оцінка",[5378,15054,15056,15066,15072,15078],{"className":15055},[14878],[5381,15057,15059,15061,15062,15065],{"className":15058},[14882],[14884,15060],{"disabled":5592,"type":14886}," Оцінити модель на ",[5213,15063,15064],{},"test set"," (не val!)",[5381,15067,15069,15071],{"className":15068},[14882],[14884,15070],{"disabled":5592,"type":14886}," Порівняти з baseline та human accuracy",[5381,15073,15075,15077],{"className":15074},[14882],[14884,15076],{"disabled":5592,"type":14886}," Проаналізувати confusion matrix",[5381,15079,15081,15083],{"className":15080},[14882],[14884,15082],{"disabled":5592,"type":14886}," Задокументувати результати",[6412,15085,15086,15091,15098],{},[5191,15087,15088],{},[5213,15089,15090],{},"Найголовніше:",[5191,15092,15093,15094,15097],{},"Машинне навчання — це ",[5213,15095,15096],{},"ітеративний процес",". Не очікуйте досягти state-of-the-art з першої спроби. Експериментуйте, аналізуйте помилки, покращуйте модель крок за кроком.",[5191,15099,15100,15101,15103],{},"Як показав цей проєкт: 43.90% → 54.80% → 66.90% → ",[5213,15102,12060],{},". Кожен крок — це progress!",[5483,15105],{},[5186,15107,15109],{"id":15108},"підсумки-модуля","Підсумки модуля",[5191,15111,15112,15113,15116],{},"Вітаємо з завершенням модуля ",[5213,15114,15115],{},"\"Батчинг, нормалізація та глибокі мережі\"","! 🎉",[5191,15118,15119],{},"Ви опанували:",[5378,15121,15122,15125,15128,15131,15134,15137,15140],{},[5381,15123,15124],{},"✅ Mini-batch Gradient Descent та ефективну обробку даних",[5381,15126,15127],{},"✅ Правильну ініціалізацію ваг (He\u002FXavier)",[5381,15129,15130],{},"✅ Batch Normalization для стабілізації навчання",[5381,15132,15133],{},"✅ Dropout та комплексні стратегії регуляризації",[5381,15135,15136],{},"✅ Проєктування глибоких мереж через Skip Connections (ResNet)",[5381,15138,15139],{},"✅ Learning Rate Scheduling для оптимальної конвергенції",[5381,15141,15142],{},"✅ Побудову production-ready класифікатора на CIFAR-100 (74% accuracy)",[5191,15144,15145,15148],{},[5213,15146,15147],{},"Це не кінець, а початок вашої подорожі у світ глибокого навчання!"," Застосовуйте вивчені техніки до власних проєктів, експериментуйте, вчіться на помилках та продовжуйте розвиватись.",[5191,15150,15151,15154],{},[5213,15152,15153],{},"Успіхів у ваших ML проєктах!"," 🚀",[15156,15157,15158],"style",{},"html pre.shiki code .s8xlr, html code.shiki .s8xlr{--shiki-light:#AF00DB;--shiki-default:#C586C0;--shiki-dark:#C586C0}html pre.shiki code .sHH4Y, html code.shiki .sHH4Y{--shiki-light:#000000;--shiki-default:#D4D4D4;--shiki-dark:#D4D4D4}html pre.shiki code .spJ8K, html code.shiki .spJ8K{--shiki-light:#008000;--shiki-default:#6A9955;--shiki-dark:#6A9955}html pre.shiki code .sJj4R, html code.shiki .sJj4R{--shiki-light:#098658;--shiki-default:#B5CEA8;--shiki-dark:#B5CEA8}html pre.shiki code .siwwj, html code.shiki .siwwj{--shiki-light:#001080;--shiki-default:#9CDCFE;--shiki-dark:#9CDCFE}html pre.shiki code .sbdoH, html code.shiki .sbdoH{--shiki-light:#A31515;--shiki-default:#CE9178;--shiki-dark:#CE9178}html pre.shiki code .su1O8, html 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проєкт на CIFAR-100 із застосуванням усіх вивчених технік. 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