Анализ сайта d2l.ai
Основное Готовность: 40%
Домен
d2l.ai
Состояние доменного имени
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Проверяем корректность доменного имени и наличие технических проблем на уровне домена.
Домен второго уровня идеален для продвижения.
Отличный запоминающийся домен.
Ответ сервера
200 Успешный ответ
HTTP-код ответа и цепочка редиректов
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Код 200 — страница доступна. Коды 3xx — редиректы (цепочки замедляют загрузку и размывают ссылочный вес). Коды 4xx/5xx — ошибки, поисковик не сможет проиндексировать страницу.
Сервер настроен корректно.
Безопасность
Сайт небезопасен
Использование HTTPS и SSL-сертификат
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HTTPS — обязательный стандарт. Google и Яндекс отдают предпочтение защищённым сайтам. Отсутствие SSL или просроченный сертификат ведут к предупреждениям в браузере и снижению позиций.
На сайте не найден защищенный протокол ssl и сайт не открывается по https!
Не настроен HSTS (Strict-Transport-Security) — рекомендуется включить.
Поздравляем! Сайт не содержится в реестре РКН.
Кодировка
utf-8
Кодировка символов страницы
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Стандарт — UTF-8. Неправильная кодировка вызывает нечитаемые символы и мешает поисковику корректно распознать текст страницы.
Указана кодировка на странице utf-8.
Язык
en
Атрибут lang в HTML-теге
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Атрибут lang (<html lang="ru">) сообщает поисковикам и браузерам, на каком языке написана страница. Помогает при ранжировании в региональном поиске.
Язык документа указан явно: en.
Скорость загрузки
~0,17сек
Время отклика сервера (TTFB)
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Time To First Byte — время до получения первого байта от сервера. Норма до 200 мс. Медленный отклик ухудшает пользовательский опыт и ранжирование: Яндекс и Google учитывают скорость страниц.
Скорость загрузки сайта 0,17сек оптимальна.
Объем документа
183Кб
Размер HTML-кода страницы
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Слишком большой HTML замедляет парсинг браузером и сканирование поисковым роботом. Рекомендуется не более 200 Кб.
Объем html-документа 183Кб оптимален.
Структура html-документа корректна.
Ресурсы
Ресурсы: 17
Внешние ресурсы страницы (CSS, JS, изображения)
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Количество и тип подключённых ресурсов влияют на скорость загрузки. Большое число запросов увеличивает время рендеринга страницы.
Кол-во файлов ресурсов 17 много для одной страницы. Приемлемо до 10. Проведите оптимизацию файлов ресурсов!
Показать полный список ресурсов
| Тип | Название | Значение |
|---|---|---|
| stylesheet | text/css | _static/material-design-lite-1.3.0/material.blue-deep_orange.min.css |
| stylesheet | text/css | _static/sphinx_materialdesign_theme.css |
| stylesheet | text/css | _static/fontawesome/all.css |
| stylesheet | text/css | _static/fonts.css |
| stylesheet | text/css | _static/pygments.css |
| stylesheet | text/css | _static/basic.css |
| stylesheet | text/css | _static/d2l.css |
| js | _static/documentation_options.js | |
| js | _static/jquery.js | |
| js | _static/underscore.js | |
| js | _static/_sphinx_javascript_frameworks_compat.js | |
| js | _static/doctools.js | |
| js | _static/sphinx_highlight.js | |
| js | _static/d2l.js | |
| js | text/javascript | _static/sphinx_materialdesign_theme.js |
| js | https://platform.twitter.com/widgets.js | |
| js | https://buttons.github.io/buttons.js |
Серверные заголовки
Кол-во: 10
HTTP-заголовки ответа сервера
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Заголовки сервера передают браузеру и поисковику служебную информацию: кеширование, безопасность (CSP, HSTS), сжатие (gzip). Правильная настройка ускоряет загрузку и повышает защищённость.
Найдены серверные заголовки 10шт. Подробнее про серверные заголовки.
Показать полный список серверных заголовков
| Ключ | Значение |
|---|---|
| Connection | keep-alive |
| x-amz-version-id | XQuk.MG09g7LhgKPD9Oj0_MN6U1QnEk0 |
| Server | AmazonS3 |
| Date | Sat, 22 Aug 2026 19:07:02 GMT |
| ETag | "ed1453444b46ec7508534cefe8eadeb7" |
| X-Cache | Hit from cloudfront |
| Via | 1.1 f046dddea42312c0568a651a5699d67e.cloudfront.net (CloudFront) |
| X-Amz-Cf-Pop | HEL51-P3 |
| X-Amz-Cf-Id | nVXRCAL897tVP9YPYQZzbX-wSaOIDvTbujjg6rMrNn86oZuyQ9UZ0Q== |
| Age | 2805 |
CMS
Не определена
Система управления сайтом (движок)
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CMS — это движок, на котором работает сайт (WordPress, 1C-Bitrix, Tilda и др.). Знание CMS помогает понять возможности SEO-оптимизации и подобрать подходящие инструменты. «Не определена» — вероятно, самописный сайт или нестандартная сборка.
CMS не определена. Вероятно, сайт самописный либо движок надёжно скрыт. Это не ошибка.
Веб-сервер
AmazonS3
Программное обеспечение сервера
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Веб-сервер — это ПО, которое отдаёт страницы посетителям (nginx, Apache, IIS, LiteSpeed и др.). Определяется по серверным заголовкам ответа (Server, X-Powered-By и т.п.). «Не определён» — сервер намеренно скрывает эти заголовки, это нормальная практика безопасности.
В заголовке Server указано: AmazonS3.
Мета-теги Готовность: 22%
Title
Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation
Заголовок страницы в браузере и поисковой выдаче
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Title — главный SEO-заголовок страницы. Влияет на CTR в поиске и ранжирование. Оптимальная длина: 50–70 символов. Ключевые слова — ближе к началу.
Устраните дубли в title: dive(2), into(2), deep(2), learning(2)
Необходимо уменьшить число символов в title (текущее значение: 75, оптимально: от 40 до 45)
Description
Описание страницы в поисковой выдаче (сниппет)
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Meta Description — текст под заголовком в выдаче. Напрямую на позиции не влияет, но влияет на CTR. Оптимальная длина: 120–160 символов.
Установите мета-тег description!
Keywords
Список ключевых слов страницы (устаревший тег)
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Meta Keywords не учитывается Яндексом и Google для ранжирования с 2009–2012 годов. Заполнение не обязательно, но не вредит. Конкурент может использовать содержимое для анализа.
Установите мета-тег keywords!
Канонический Url
Указывает поисковику основную версию страницы
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Canonical (rel=canonical) предотвращает проблему дублей страниц. Должен точно совпадать с URL проверяемой страницы. Неправильный canonical может передать ссылочный вес на другую страницу.
Рекомендуем прописать канонический Url.
Robots
Ошибок нет
Директивы для поисковых роботов на уровне страницы
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Meta Robots управляет индексацией конкретной страницы: index/noindex — индексировать ли, follow/nofollow — следовать ли по ссылкам. Noindex полностью исключает страницу из поиска.
Meta-тег robots не указан. Страница свободна для индексации.
Адаптивность
width=device-width, initial-scale=1, shrink-to-fit=no
Настройка масштабирования на мобильных устройствах
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Тег viewport (<meta name="viewport">) сообщает браузеру, как масштабировать страницу на мобильных. Стандарт: width=device-width, initial-scale=1. Отсутствие — признак отсутствия мобильной версии.
Meta-тег viewport со значением-константой width=device-width задаёт ширину страницы в соответствии с размером экрана.
Meta-тег viewport со значением initial-scale=1.0 определяет масштаб 1:1, т.е. «не масштабировать».
Разметка OpenGraph
Не найдено
Мета-теги для красивых превью в соцсетях
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OpenGraph (og:title, og:description, og:image) управляет тем, как страница выглядит при репосте в социальных сетях и мессенджерах. Отсутствие OG-тегов — невзрачный превью при шеринге.
Разметка OpenGraph не задана. Страница не оптимизирована под социальные сети. Мета-теги с разметкой Og помогают социальным роботам лучше структурировать Ваш сайт.
Все мета-теги
Кол-во: 2
Полный список мета-тегов страницы
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Таблица всех meta-тегов, включая нестандартные. Позволяет найти опечатки, дубли и лишние теги.
Найдены мета-теги 2шт. Мета-теги не видимы для человека и предназначены для обмена информацией между веб-страницей и поисковыми системами, браузерами и другими веб-службами. С ними роботы 🤖 и устройства ведут себя более ожидаемо.
Показать полный список мета-тегов
| Тип | Название | Значение |
|---|---|---|
| name | viewport | width=device-width, initial-scale=1.0 |
| name | viewport | width=device-width, initial-scale=1, shrink-to-fit=no |
Оптимизация Готовность: 77%
Структура
Ошибок нет
Семантические HTML-элементы страницы
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Проверяет наличие основных структурных элементов: nav, header, footer, main. Корректная семантическая структура помогает поисковику понять архитектуру страницы.
Структура документа корректна (теги <html> и <body> присутствуют по одному на документ).
Контент
Есть ошибки
Объём и качество текстового содержимого
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Анализирует объём полезного текста на странице. Слишком мало — страница может считаться малополезной. Слишком много — ухудшается читаемость и восприятие.
Среднее число слов в абзаце 9 слишком мало. Сделайте контент более читаемым!
Слова из title 5 встречаются в тексте достаточно.
Абзацев с текстом 25 достаточно.
Кол-во знаков контента 34698 на странице оптимально.
Кол-во слов 4410 на странице оптимально.
Заголовки
Ошибок нет
Иерархия заголовков H1–H6
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H1 должен быть один и содержать ключевой запрос. H2–H6 описывают подразделы. Пропуск уровней (H1 → H3) и несколько H1 — типичные ошибки, снижающие понятность страницы для поисковика.
На странице присутствуют заголовки <h1> 1. Это прекрасно.
На странице присутствуют заголовки <h2> 9. Это хорошо.
На странице присутствуют заголовки <h3> 19.
Тошнота
15,59
Насколько одно слово доминирует в тексте
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Классическая тошнота = √(частота самого повторяющегося слова). Норма до 7–8: текст воспринимается естественно. Выше — поисковик может счесть страницу переспамленной.
Тошнота превышает норму 5. Измените текст страницы!
Академич. тошнота
3,11%
Насколько текст перенасыщен ключевыми словами
?
Академическая тошнота = (частота слова / общее количество слов) × 100%. Показывает долю конкретного слова в тексте. Норма 5–15%.
Академическая тошнота не дотягивает до нормы 5-15%. Измените текст страницы!
Семантическое ядро
20
Наиболее часто встречающиеся слова на странице
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Топ слов по частоте использования. Показывает, какие слова доминируют в тексте с точки зрения поисковика.
Контент страницы содержит осмысленный текст и слова.
Показать список слов
| Слово | Кол-во | Частота |
|---|---|---|
| university | 243 | 5,51% |
| networks | 66 | 1,50% |
| neural | 51 | 1,16% |
| technology | 47 | 1,07% |
| learning | 37 | 0,84% |
| institute | 35 | 0,79% |
| implementation | 29 | 0,66% |
| dataset | 27 | 0,61% |
| recurrent | 27 | 0,61% |
| regression | 24 | 0,54% |
| universidad | 22 | 0,50% |
| linear | 21 | 0,48% |
| convolutional | 21 | 0,48% |
| amazon | 20 | 0,45% |
| national | 20 | 0,45% |
| pretraining | 19 | 0,43% |
| optimization | 19 | 0,43% |
| language | 18 | 0,41% |
| attention | 18 | 0,41% |
| recommender | 17 | 0,39% |
Индексация Готовность: 0%
Индексирование
Есть ошибки
Разрешено ли индексирование страницы
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Проверяет, не закрыта ли страница от индексации через robots.txt, meta robots или X-Robots-Tag. Страница, закрытая от индексации, не появится в поисковой выдаче.
Анкоров на странице 655 слишком много. Проведите ревизию и оптимизацию ссылок сайта.
Robots.txt
Найден корректный robots.txt
Файл управления сканированием сайта роботами
?
Robots.txt указывает поисковым роботам, какие страницы сканировать, а какие — нет. Ошибки в файле могут случайно закрыть важные разделы от индексации.
Robots.txt настроен корректно. Размер файла: 188248 байт. Загружен за: 0сек.
Проверяемая страница не запрещена в robots.txt.
Robots.txt доступен по постоянному адресу
Показать содержимое robots.txt
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation</title>
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<span class="mdl-layout-title toc">Table Of Contents</span>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="chapter_preface/index.html">Preface</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_installation/index.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_notation/index.html">Notation</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="chapter_introduction/index.html">1. Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_preliminaries/index.html">2. Preliminaries</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/ndarray.html">2.1. Data Manipulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/pandas.html">2.2. Data Preprocessing</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/linear-algebra.html">2.3. Linear Algebra</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/calculus.html">2.4. Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/autograd.html">2.5. Automatic Differentiation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/probability.html">2.6. Probability and Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/lookup-api.html">2.7. Documentation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-regression/index.html">3. Linear Neural Networks for Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression.html">3.1. Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/oo-design.html">3.2. Object-Oriented Design for Implementation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/synthetic-regression-data.html">3.3. Synthetic Regression Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-scratch.html">3.4. Linear Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-concise.html">3.5. Concise Implementation of Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/generalization.html">3.6. Generalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/weight-decay.html">3.7. Weight Decay</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-classification/index.html">4. Linear Neural Networks for Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression.html">4.1. Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/image-classification-dataset.html">4.2. The Image Classification Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/classification.html">4.3. The Base Classification Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-scratch.html">4.4. Softmax Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-concise.html">4.5. Concise Implementation of Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/generalization-classification.html">4.6. Generalization in Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/environment-and-distribution-shift.html">4.7. Environment and Distribution Shift</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_multilayer-perceptrons/index.html">5. Multilayer Perceptrons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp.html">5.1. Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp-implementation.html">5.2. Implementation of Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/backprop.html">5.3. Forward Propagation, Backward Propagation, and Computational Graphs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/numerical-stability-and-init.html">5.4. Numerical Stability and Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/generalization-deep.html">5.5. Generalization in Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/dropout.html">5.6. Dropout</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/kaggle-house-price.html">5.7. Predicting House Prices on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_builders-guide/index.html">6. Builders’ Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/model-construction.html">6.1. Layers and Modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/parameters.html">6.2. Parameter Management</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/init-param.html">6.3. Parameter Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/lazy-init.html">6.4. Lazy Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/custom-layer.html">6.5. Custom Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/read-write.html">6.6. File I/O</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/use-gpu.html">6.7. GPUs</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-neural-networks/index.html">7. Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/why-conv.html">7.1. From Fully Connected Layers to Convolutions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/conv-layer.html">7.2. Convolutions for Images</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/padding-and-strides.html">7.3. Padding and Stride</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/channels.html">7.4. Multiple Input and Multiple Output Channels</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/pooling.html">7.5. Pooling</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/lenet.html">7.6. Convolutional Neural Networks (LeNet)</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-modern/index.html">8. Modern Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/alexnet.html">8.1. Deep Convolutional Neural Networks (AlexNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/vgg.html">8.2. Networks Using Blocks (VGG)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/nin.html">8.3. Network in Network (NiN)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/googlenet.html">8.4. Multi-Branch Networks (GoogLeNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/batch-norm.html">8.5. Batch Normalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/resnet.html">8.6. Residual Networks (ResNet) and ResNeXt</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/densenet.html">8.7. Densely Connected Networks (DenseNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/cnn-design.html">8.8. Designing Convolution Network Architectures</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-neural-networks/index.html">9. Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/sequence.html">9.1. Working with Sequences</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/text-sequence.html">9.2. Converting Raw Text into Sequence Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/language-model.html">9.3. Language Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn.html">9.4. Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-scratch.html">9.5. Recurrent Neural Network Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-concise.html">9.6. Concise Implementation of Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/bptt.html">9.7. Backpropagation Through Time</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-modern/index.html">10. Modern Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/lstm.html">10.1. Long Short-Term Memory (LSTM)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/gru.html">10.2. Gated Recurrent Units (GRU)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/deep-rnn.html">10.3. Deep Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/bi-rnn.html">10.4. Bidirectional Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/machine-translation-and-dataset.html">10.5. Machine Translation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/encoder-decoder.html">10.6. The Encoder–Decoder Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/seq2seq.html">10.7. Sequence-to-Sequence Learning for Machine Translation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/beam-search.html">10.8. Beam Search</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/index.html">11. Attention Mechanisms and Transformers</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/queries-keys-values.html">11.1. Queries, Keys, and Values</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-pooling.html">11.2. Attention Pooling by Similarity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-scoring-functions.html">11.3. Attention Scoring Functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/bahdanau-attention.html">11.4. The Bahdanau Attention Mechanism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/multihead-attention.html">11.5. Multi-Head Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/self-attention-and-positional-encoding.html">11.6. Self-Attention and Positional Encoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/transformer.html">11.7. The Transformer Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/vision-transformer.html">11.8. Transformers for Vision</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/large-pretraining-transformers.html">11.9. Large-Scale Pretraining with Transformers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_optimization/index.html">12. Optimization Algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/optimization-intro.html">12.1. Optimization and Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/convexity.html">12.2. Convexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/gd.html">12.3. Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/sgd.html">12.4. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/minibatch-sgd.html">12.5. Minibatch Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/momentum.html">12.6. Momentum</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adagrad.html">12.7. Adagrad</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/rmsprop.html">12.8. RMSProp</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adadelta.html">12.9. Adadelta</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adam.html">12.10. Adam</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/lr-scheduler.html">12.11. Learning Rate Scheduling</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computational-performance/index.html">13. Computational Performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hybridize.html">13.1. Compilers and Interpreters</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/async-computation.html">13.2. Asynchronous Computation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/auto-parallelism.html">13.3. Automatic Parallelism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hardware.html">13.4. Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus.html">13.5. Training on Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus-concise.html">13.6. Concise Implementation for Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/parameterserver.html">13.7. Parameter Servers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computer-vision/index.html">14. Computer Vision</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/image-augmentation.html">14.1. Image Augmentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fine-tuning.html">14.2. Fine-Tuning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/bounding-box.html">14.3. Object Detection and Bounding Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/anchor.html">14.4. Anchor Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/multiscale-object-detection.html">14.5. Multiscale Object Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/object-detection-dataset.html">14.6. The Object Detection Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/ssd.html">14.7. Single Shot Multibox Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/rcnn.html">14.8. Region-based CNNs (R-CNNs)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/semantic-segmentation-and-dataset.html">14.9. Semantic Segmentation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/transposed-conv.html">14.10. Transposed Convolution</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fcn.html">14.11. Fully Convolutional Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/neural-style.html">14.12. Neural Style Transfer</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-cifar10.html">14.13. Image Classification (CIFAR-10) on Kaggle</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-dog.html">14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-pretraining/index.html">15. Natural Language Processing: Pretraining</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec.html">15.1. Word Embedding (word2vec)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/approx-training.html">15.2. Approximate Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word-embedding-dataset.html">15.3. The Dataset for Pretraining Word Embeddings</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec-pretraining.html">15.4. Pretraining word2vec</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/glove.html">15.5. Word Embedding with Global Vectors (GloVe)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/subword-embedding.html">15.6. Subword Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/similarity-analogy.html">15.7. Word Similarity and Analogy</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert.html">15.8. Bidirectional Encoder Representations from Transformers (BERT)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-dataset.html">15.9. The Dataset for Pretraining BERT</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-pretraining.html">15.10. Pretraining BERT</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-applications/index.html">16. Natural Language Processing: Applications</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-and-dataset.html">16.1. Sentiment Analysis and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-rnn.html">16.2. Sentiment Analysis: Using Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-cnn.html">16.3. Sentiment Analysis: Using Convolutional Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html">16.4. Natural Language Inference and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-attention.html">16.5. Natural Language Inference: Using Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/finetuning-bert.html">16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-bert.html">16.7. Natural Language Inference: Fine-Tuning BERT</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_reinforcement-learning/index.html">17. Reinforcement Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/mdp.html">17.1. Markov Decision Process (MDP)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/value-iter.html">17.2. Value Iteration</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/qlearning.html">17.3. Q-Learning</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_gaussian-processes/index.html">18. Gaussian Processes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-intro.html">18.1. Introduction to Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-priors.html">18.2. Gaussian Process Priors</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-inference.html">18.3. Gaussian Process Inference</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_hyperparameter-optimization/index.html">19. Hyperparameter Optimization</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-intro.html">19.1. What Is Hyperparameter Optimization?</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-api.html">19.2. Hyperparameter Optimization API</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/rs-async.html">19.3. Asynchronous Random Search</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-intro.html">19.4. Multi-Fidelity Hyperparameter Optimization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-async.html">19.5. Asynchronous Successive Halving</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_generative-adversarial-networks/index.html">20. Generative Adversarial Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/gan.html">20.1. Generative Adversarial Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/dcgan.html">20.2. Deep Convolutional Generative Adversarial Networks</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recommender-systems/index.html">21. Recommender Systems</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/recsys-intro.html">21.1. Overview of Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/movielens.html">21.2. The MovieLens Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/mf.html">21.3. Matrix Factorization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/autorec.html">21.4. AutoRec: Rating Prediction with Autoencoders</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ranking.html">21.5. Personalized Ranking for Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/neumf.html">21.6. Neural Collaborative Filtering for Personalized Ranking</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/seqrec.html">21.7. Sequence-Aware Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ctr.html">21.8. Feature-Rich Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/fm.html">21.9. Factorization Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/deepfm.html">21.10. Deep Factorization Machines</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/index.html">22. Appendix: Mathematics for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/geometry-linear-algebraic-ops.html">22.1. Geometry and Linear Algebraic Operations</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/eigendecomposition.html">22.2. Eigendecompositions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/single-variable-calculus.html">22.3. Single Variable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/multivariable-calculus.html">22.4. Multivariable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/integral-calculus.html">22.5. Integral Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/random-variables.html">22.6. Random Variables</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/maximum-likelihood.html">22.7. Maximum Likelihood</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/distributions.html">22.8. Distributions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/naive-bayes.html">22.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/statistics.html">22.10. Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/information-theory.html">22.11. Information Theory</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/index.html">23. Appendix: Tools for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/jupyter.html">23.1. Using Jupyter Notebooks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/sagemaker.html">23.2. Using Amazon SageMaker</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/aws.html">23.3. Using AWS EC2 Instances</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/colab.html">23.4. Using Google Colab</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/selecting-servers-gpus.html">23.5. Selecting Servers and GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/contributing.html">23.6. Contributing to This Book</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/utils.html">23.7. Utility Functions and Classes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/d2l.html">23.8. The <code class="docutils literal notranslate"><span class="pre">d2l</span></code> API Document</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_preface/index.html">Preface</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_installation/index.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_notation/index.html">Notation</a></li>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="chapter_introduction/index.html">1. Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_preliminaries/index.html">2. Preliminaries</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/ndarray.html">2.1. Data Manipulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/pandas.html">2.2. Data Preprocessing</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/linear-algebra.html">2.3. Linear Algebra</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/calculus.html">2.4. Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/autograd.html">2.5. Automatic Differentiation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/probability.html">2.6. Probability and Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/lookup-api.html">2.7. Documentation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-regression/index.html">3. Linear Neural Networks for Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression.html">3.1. Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/oo-design.html">3.2. Object-Oriented Design for Implementation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/synthetic-regression-data.html">3.3. Synthetic Regression Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-scratch.html">3.4. Linear Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-concise.html">3.5. Concise Implementation of Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/generalization.html">3.6. Generalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/weight-decay.html">3.7. Weight Decay</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-classification/index.html">4. Linear Neural Networks for Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression.html">4.1. Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/image-classification-dataset.html">4.2. The Image Classification Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/classification.html">4.3. The Base Classification Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-scratch.html">4.4. Softmax Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-concise.html">4.5. Concise Implementation of Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/generalization-classification.html">4.6. Generalization in Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/environment-and-distribution-shift.html">4.7. Environment and Distribution Shift</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_multilayer-perceptrons/index.html">5. Multilayer Perceptrons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp.html">5.1. Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp-implementation.html">5.2. Implementation of Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/backprop.html">5.3. Forward Propagation, Backward Propagation, and Computational Graphs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/numerical-stability-and-init.html">5.4. Numerical Stability and Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/generalization-deep.html">5.5. Generalization in Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/dropout.html">5.6. Dropout</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/kaggle-house-price.html">5.7. Predicting House Prices on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_builders-guide/index.html">6. Builders’ Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/model-construction.html">6.1. Layers and Modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/parameters.html">6.2. Parameter Management</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/init-param.html">6.3. Parameter Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/lazy-init.html">6.4. Lazy Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/custom-layer.html">6.5. Custom Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/read-write.html">6.6. File I/O</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/use-gpu.html">6.7. GPUs</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-neural-networks/index.html">7. Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/why-conv.html">7.1. From Fully Connected Layers to Convolutions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/conv-layer.html">7.2. Convolutions for Images</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/padding-and-strides.html">7.3. Padding and Stride</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/channels.html">7.4. Multiple Input and Multiple Output Channels</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/pooling.html">7.5. Pooling</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/lenet.html">7.6. Convolutional Neural Networks (LeNet)</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-modern/index.html">8. Modern Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/alexnet.html">8.1. Deep Convolutional Neural Networks (AlexNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/vgg.html">8.2. Networks Using Blocks (VGG)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/nin.html">8.3. Network in Network (NiN)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/googlenet.html">8.4. Multi-Branch Networks (GoogLeNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/batch-norm.html">8.5. Batch Normalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/resnet.html">8.6. Residual Networks (ResNet) and ResNeXt</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/densenet.html">8.7. Densely Connected Networks (DenseNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/cnn-design.html">8.8. Designing Convolution Network Architectures</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-neural-networks/index.html">9. Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/sequence.html">9.1. Working with Sequences</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/text-sequence.html">9.2. Converting Raw Text into Sequence Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/language-model.html">9.3. Language Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn.html">9.4. Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-scratch.html">9.5. Recurrent Neural Network Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-concise.html">9.6. Concise Implementation of Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/bptt.html">9.7. Backpropagation Through Time</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-modern/index.html">10. Modern Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/lstm.html">10.1. Long Short-Term Memory (LSTM)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/gru.html">10.2. Gated Recurrent Units (GRU)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/deep-rnn.html">10.3. Deep Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/bi-rnn.html">10.4. Bidirectional Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/machine-translation-and-dataset.html">10.5. Machine Translation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/encoder-decoder.html">10.6. The Encoder–Decoder Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/seq2seq.html">10.7. Sequence-to-Sequence Learning for Machine Translation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/beam-search.html">10.8. Beam Search</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/index.html">11. Attention Mechanisms and Transformers</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/queries-keys-values.html">11.1. Queries, Keys, and Values</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-pooling.html">11.2. Attention Pooling by Similarity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-scoring-functions.html">11.3. Attention Scoring Functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/bahdanau-attention.html">11.4. The Bahdanau Attention Mechanism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/multihead-attention.html">11.5. Multi-Head Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/self-attention-and-positional-encoding.html">11.6. Self-Attention and Positional Encoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/transformer.html">11.7. The Transformer Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/vision-transformer.html">11.8. Transformers for Vision</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/large-pretraining-transformers.html">11.9. Large-Scale Pretraining with Transformers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_optimization/index.html">12. Optimization Algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/optimization-intro.html">12.1. Optimization and Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/convexity.html">12.2. Convexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/gd.html">12.3. Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/sgd.html">12.4. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/minibatch-sgd.html">12.5. Minibatch Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/momentum.html">12.6. Momentum</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adagrad.html">12.7. Adagrad</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/rmsprop.html">12.8. RMSProp</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adadelta.html">12.9. Adadelta</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adam.html">12.10. Adam</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/lr-scheduler.html">12.11. Learning Rate Scheduling</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computational-performance/index.html">13. Computational Performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hybridize.html">13.1. Compilers and Interpreters</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/async-computation.html">13.2. Asynchronous Computation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/auto-parallelism.html">13.3. Automatic Parallelism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hardware.html">13.4. Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus.html">13.5. Training on Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus-concise.html">13.6. Concise Implementation for Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/parameterserver.html">13.7. Parameter Servers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computer-vision/index.html">14. Computer Vision</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/image-augmentation.html">14.1. Image Augmentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fine-tuning.html">14.2. Fine-Tuning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/bounding-box.html">14.3. Object Detection and Bounding Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/anchor.html">14.4. Anchor Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/multiscale-object-detection.html">14.5. Multiscale Object Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/object-detection-dataset.html">14.6. The Object Detection Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/ssd.html">14.7. Single Shot Multibox Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/rcnn.html">14.8. Region-based CNNs (R-CNNs)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/semantic-segmentation-and-dataset.html">14.9. Semantic Segmentation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/transposed-conv.html">14.10. Transposed Convolution</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fcn.html">14.11. Fully Convolutional Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/neural-style.html">14.12. Neural Style Transfer</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-cifar10.html">14.13. Image Classification (CIFAR-10) on Kaggle</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-dog.html">14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-pretraining/index.html">15. Natural Language Processing: Pretraining</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec.html">15.1. Word Embedding (word2vec)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/approx-training.html">15.2. Approximate Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word-embedding-dataset.html">15.3. The Dataset for Pretraining Word Embeddings</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec-pretraining.html">15.4. Pretraining word2vec</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/glove.html">15.5. Word Embedding with Global Vectors (GloVe)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/subword-embedding.html">15.6. Subword Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/similarity-analogy.html">15.7. Word Similarity and Analogy</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert.html">15.8. Bidirectional Encoder Representations from Transformers (BERT)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-dataset.html">15.9. The Dataset for Pretraining BERT</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-pretraining.html">15.10. Pretraining BERT</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-applications/index.html">16. Natural Language Processing: Applications</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-and-dataset.html">16.1. Sentiment Analysis and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-rnn.html">16.2. Sentiment Analysis: Using Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-cnn.html">16.3. Sentiment Analysis: Using Convolutional Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html">16.4. Natural Language Inference and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-attention.html">16.5. Natural Language Inference: Using Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/finetuning-bert.html">16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-bert.html">16.7. Natural Language Inference: Fine-Tuning BERT</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_reinforcement-learning/index.html">17. Reinforcement Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/mdp.html">17.1. Markov Decision Process (MDP)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/value-iter.html">17.2. Value Iteration</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/qlearning.html">17.3. Q-Learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_gaussian-processes/index.html">18. Gaussian Processes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-intro.html">18.1. Introduction to Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-priors.html">18.2. Gaussian Process Priors</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-inference.html">18.3. Gaussian Process Inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_hyperparameter-optimization/index.html">19. Hyperparameter Optimization</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-intro.html">19.1. What Is Hyperparameter Optimization?</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-api.html">19.2. Hyperparameter Optimization API</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/rs-async.html">19.3. Asynchronous Random Search</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-intro.html">19.4. Multi-Fidelity Hyperparameter Optimization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-async.html">19.5. Asynchronous Successive Halving</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_generative-adversarial-networks/index.html">20. Generative Adversarial Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/gan.html">20.1. Generative Adversarial Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/dcgan.html">20.2. Deep Convolutional Generative Adversarial Networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_recommender-systems/index.html">21. Recommender Systems</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/recsys-intro.html">21.1. Overview of Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/movielens.html">21.2. The MovieLens Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/mf.html">21.3. Matrix Factorization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/autorec.html">21.4. AutoRec: Rating Prediction with Autoencoders</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ranking.html">21.5. Personalized Ranking for Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/neumf.html">21.6. Neural Collaborative Filtering for Personalized Ranking</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/seqrec.html">21.7. Sequence-Aware Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ctr.html">21.8. Feature-Rich Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/fm.html">21.9. Factorization Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/deepfm.html">21.10. Deep Factorization Machines</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/index.html">22. Appendix: Mathematics for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/geometry-linear-algebraic-ops.html">22.1. Geometry and Linear Algebraic Operations</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/eigendecomposition.html">22.2. Eigendecompositions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/single-variable-calculus.html">22.3. Single Variable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/multivariable-calculus.html">22.4. Multivariable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/integral-calculus.html">22.5. Integral Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/random-variables.html">22.6. Random Variables</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/maximum-likelihood.html">22.7. Maximum Likelihood</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/distributions.html">22.8. Distributions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/naive-bayes.html">22.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/statistics.html">22.10. Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/information-theory.html">22.11. Information Theory</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/index.html">23. Appendix: Tools for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/jupyter.html">23.1. Using Jupyter Notebooks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/sagemaker.html">23.2. Using Amazon SageMaker</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/aws.html">23.3. Using AWS EC2 Instances</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/colab.html">23.4. Using Google Colab</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/selecting-servers-gpus.html">23.5. Selecting Servers and GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/contributing.html">23.6. Contributing to This Book</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/utils.html">23.7. Utility Functions and Classes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/d2l.html">23.8. The <code class="docutils literal notranslate"><span class="pre">d2l</span></code> API Document</a></li>
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<h2>Dive into Deep Learning</h2>
<p><b>Interactive</b> deep learning book with code, math, and discussions <br><br>
Implemented with <b>PyTorch</b>, <b>NumPy/MXNet</b>, <b>JAX</b>, and <b>TensorFlow</b><br><br>
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<li>[Feb 2023]
The book is forthcoming on Cambridge University Press (<a href="https://www.amazon.com/Dive-into-Learning-Aston-Zhang/dp/1009389432/">order</a>).
The Chinese version is the <a href="https://raw.githubusercontent.com/d2l-ai/d2l-zh/master/static/frontpage/_images/sales/jd-2023020304-all-ai-zh-4.png">best seller</a> at the largest Chinese online bookstore.
Follow D2L's <a href="https://github.com/d2l-ai/d2l-en">open-source project</a> for the latest updates.
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<li> [Dec 2022] JAX implementation is available! New topics of <a href="chapter_reinforcement-learning/index.html">reinforcement learning</a>,
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transformers for <a href="chapter_attention-mechanisms-and-transformers/vision-transformer.html">vision</a>
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<div class = "authors mdl-grid" id = "author" >
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<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/aston.jpg"/>
<h3><a href="https://www.astonzhang.com/">Aston Zhang</a></h3>
<p>Amazon</p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/zack.jpg"/>
<h3><a href="http://zacklipton.com/">Zack C. Lipton</a></h3>
<p>CMU and Amazon</p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/mu.jpg"/>
<h3><a href="https://www.cs.cmu.edu/~muli/">Mu Li</a></h3>
<p>Amazon</p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/alex.jpg"/>
<h3><a href="https://alex.smola.org/">Alex J. Smola</a></h3>
<p>Amazon</p>
</div>
</div>
<div class = "author-group-title mdl-cell mdl-cell--12-col mdl-cell--top">
<h2>Vol.2 Chapter Authors</h2>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/pratik.jpg"/>
<h3><a href="https://pratikac.github.io/">Pratik Chaudhari</a></h3>
<p>UPenn and Amazon<br><i><a href="chapter_reinforcement-learning/index.html">Reinforcement Learning</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/rasool.jpg"/>
<h3><a href="https://sites.google.com/site/rfakoor">Rasool Fakoor</a></h3>
<p>Amazon<br><i><a href="chapter_reinforcement-learning/index.html">Reinforcement Learning</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/kavosh.jpg"/>
<h3><a href="https://cs.brown.edu/~kasadiat/">Kavosh Asadi</a></h3>
<p>Amazon<br><i><a href="chapter_reinforcement-learning/index.html">Reinforcement Learning</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/andrew.jpg"/>
<h3><a href="https://cims.nyu.edu/~andrewgw/">Andrew Gordon Wilson</a></h3>
<p>NYU and Amazon<br><i><a href="chapter_gaussian-processes/index.html">Gaussian Processes</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/aaron.jpg"/>
<h3><a href="https://aaronkl.github.io/">Aaron Klein</a></h3>
<p>Amazon<br><i><a href="chapter_hyperparameter-optimization/index.html">Hyperparameter Optimization</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/matthias.jpg"/>
<h3><a href="https://mseeger.github.io/">Matthias Seeger</a></h3>
<p>Amazon<br><i><a href="chapter_hyperparameter-optimization/index.html">Hyperparameter Optimization</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/cedric.png"/>
<h3><a href="http://www0.cs.ucl.ac.uk/staff/c.archambeau/">Cedric Archambeau</a></h3>
<p>Amazon<br><i><a href="chapter_hyperparameter-optimization/index.html">Hyperparameter Optimization</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/shuai.jpg"/>
<h3><a href="https://shuaizhang.tech/">Shuai Zhang</a></h3>
<p>Amazon
<br><i><a href="chapter_recommender-systems/index.html">Recommender Systems</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/yi.jpg"/>
<h3><a href="https://vanzytay.github.io/">Yi Tay</a></h3>
<p>Google
<br><i><a href="chapter_recommender-systems/index.html">Recommender Systems</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/brent.jpg"/>
<h3><a href="https://www.linkedin.com/in/brent-werness-1506471b7/">Brent Werness</a></h3>
<p>Amazon<br><i><a href="chapter_appendix-mathematics-for-deep-learning/index.html">Mathematics
for Deep Learning</a></i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/rachel.jpeg"/>
<h3><a href="https://www.linkedin.com/in/rachelsonghu/">Rachel Hu</a></h3>
<p>Amazon<br><i><a href="chapter_appendix-mathematics-for-deep-learning/index.html">Mathematics
for Deep Learning</a></i></p>
</div>
</div>
<div class = "author-group-title mdl-cell mdl-cell--12-col mdl-cell--top">
<h2>Framework Adaptation Authors</h2>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/anirudh.jpg"/>
<h3><a href="https://github.com/AnirudhDagar">Anirudh Dagar</a></h3>
<p>Amazon<br><i>PyTorch Adaptation</i><br><i>JAX Adaptation</i></p>
</div>
</div>
<div class = "mdl-cell mdl-cell--3-col mdl-cell--top">
<div class="author-item">
<img src="./_images/yuan.jpg"/>
<h3><a href="https://terrytangyuan.github.io/about/">Yuan Tang</a></h3>
<p>Akuity<br><i>TensorFlow Adaptation</i></p>
</div>
</div>
<div class = "author-group-title mdl-cell mdl-cell--12-col mdl-cell--top">
<h3> We thank all the <a href="https://github.com/d2l-ai/d2l-en/graphs/contributors">community contributors</a><br>for making this open source book better for everyone.</h3>
<h4><a href="https://d2l.ai/chapter_appendix-tools-for-deep-learning/contributing.html">Contribute to the book</a></h4>
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['Mississippi State University', 33.45519208318904, -88.79438733102509],
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['National Institute of Technical Teachers Training & Research', 30.729031, 76.807565],
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['National Institute of Technology, Warangal', 17.983721, 79.530946],
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['National Taichung University of Science and Technology', 24.149733494481218, 120.68374973998229],
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['New Jersey Institute of Technology', 40.74236243668042, -74.1793117730986],
['New Mexico Institute of Mining and Technology', 34.065975205125405, -106.90560027324813],
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['Thapar Institute of Engineering and Technology', 30.356403, 76.364800],
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['Universidad del Norte, Colombia', 11.019500454164058, -74.85043163130192],
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['Universidad Nacional de Colombia Sede Manizales', 5.056096, -75.491125],
['Universidad Nacional de Tierra del Fuego', -54.81885380138744, -68.32556169447504],
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['Vardhman Mahaveer Open University', 25.133687148968978, 75.81073254001238],
['Vietnamese-German University', 11.054337, 106.665432],
['Vignana Jyothi Institute Of Management', 17.53968451796987, 78.3869266130151],
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['Yıldız Teknik Üniversitesi', 41.05257981870814, 29.011524197289017],
['Yonsei University', 37.56588603067197, 126.93857199799072],
['Yunnan University',25.053496, 102.703883],
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Abasyn University, Islamabad Campus<br>
Alexandria University<br>
Amirkabir University of Technology<br>
Amity University<br>
Amrita Vishwa Vidyapeetham University<br>
Anna University<br>
Anna University Regional Campus Madurai<br>
Ateneo de Naga University<br>
Australian National University<br>
Bar-Ilan University<br>
Barnard College<br>
Beijing Foresty University<br>
Birla Institute of Technology and Science, Hyderabad<br>
Birla Institute of Technology and Science, Pilani<br>
BML Munjal University<br>
Boston College<br>
Boston University<br>
Brac University<br>
Brandeis University<br>
Brown University<br>
Brunel University London<br>
Cairo University<br>
California State University, Northridge<br>
Cankaya University<br>
Carnegie Mellon University<br>
Center for Research and Advanced Studies of the National Polytechnic Institute<br>
Chalmers University of Technology<br>
Chennai Mathematical Institute<br>
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City College of New York<br>
City University of Hong Kong<br>
City University of Science and Information Technology<br>
College of Engineering Pune<br>
Columbia University<br>
Cornell University<br>
Cyprus Institute<br>
Deakin University<br>
Diponegoro University<br>
Dresden University of Technology<br>
Duke University<br>
Durban University of Technology<br>
Eastern Mediterranean University<br>
Ecole Nationale Supérieure d'Informatique<br>
Ecole Nationale Supérieure de Cognitique<br>
École Nationale Supérieure de Techniques Avancées<br>
Eindhoven University of Technology<br>
Emory University<br>
Eötvös Loránd University<br>
Escuela Politécnica Nacional<br>
Escuela Superior Politecnica del Litoral<br>
Federal University Lokoja<br>
Feng Chia University<br>
Fisk University<br>
Florida Atlantic University<br>
FPT University<br>
Fudan University<br>
Ganpat University<br>
Gayatri Vidya Parishad College of Engineering (Autonomous)<br>
Gazi Üniversitesi<br>
Gdańsk University of Technology<br>
George Mason University<br>
Georgetown University<br>
Georgia Institute of Technology<br>
Gheorghe Asachi Technical University of Iaşi<br>
Golden Gate University<br>
Great Lakes Institute of Management<br>
Gwangju Institute of Science and Technology<br>
Habib University<br>
Hamad Bin Khalifa University<br>
Hangzhou Dianzi University<br>
Hangzhou Dianzi University<br>
Hankuk University of Foreign Studies<br>
Harare Institute of Technology<br>
Harbin Institute of Technology<br>
Harvard University<br>
Hasso-Plattner-Institut<br>
Hebrew University of Jerusalem<br>
Heinrich-Heine-Universität Düsseldorf<br>
Henan Institute of Technology<br>
Hertie School<br>
Higher Institute of Applied Science and Technology of Sousse<br>
Hiroshima University<br>
Ho Chi Minh City University of Foreign Languages and Information Technology<br>
Hochschule Bremen<br>
Hochschule für Technik und Wirtschaft<br>
Hochschule Hamm-Lippstadt<br>
Hong Kong University of Science and Technology<br>
Houston Community College<br>
Huazhong University of Science and Technology<br>
Humboldt-Universität zu Berlin<br>
İbn Haldun Üniversitesi<br>
Icahn School of Medicine at Mount Sinai<br>
Imperial College London<br>
IMT Mines Alès<br>
Indian Institute of Technology Bombay<br>
Indian Institute of Technology Hyderabad<br>
Indian Institute of Technology Jodhpur<br>
Indian Institute of Technology Kanpur<br>
Indian Institute of Technology Kharagpur<br>
Indian Institute of Technology Mandi<br>
Indian Institute of Technology Ropar<br>
Indian School of Business<br>
Indira Gandhi National Open University<br>
Indraprastha Institute of Information Technology, Delhi<br>
Institut catholique d'arts et métiers (ICAM)<br>
Institut de recherche en informatique de Toulouse<br>
Institut Supérieur d'Informatique et des Techniques de Communication<br>
Institut Supérieur De L'electronique Et Du Numérique<br>
Institut Teknologi Bandung<br>
Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, Campus Salto<br>
Instituto Politécnico Nacional<br>
Instituto Tecnológico Autónomo de México<br>
Instituto Tecnológico de Buenos Aires<br>
Islamic University of Medina<br>
İstanbul Teknik Üniversitesi<br>
IT-Universitetet i København<br>
Ivan Franko National University of Lviv<br>
Jeonbuk National Univerity<br>
Johns Hopkins University<br>
Julius-Maximilians-Universität Würzburg<br>
Keio University<br>
King Abdullah University of Science and Technology<br>
King Fahd University of Petroleum and Minerals<br>
King Faisal University<br>
Kongu Engineering College<br>
Korea Aerospace University<br>
KPR Institute of Engineering and Technology<br>
Kyungpook National University<br>
Lancaster University<br>
Leading Unviersity<br>
Leibniz Universität Hannover<br>
Leuphana University of Lüneburg<br>
London School of Economics & Political Science<br>
M.S.Ramaiah University of Applied Sciences<br>
Make School<br>
Masaryk University<br>
Massachusetts Institute of Technology<br>
Maynooth University<br>
McGill University<br>
Menoufia University<br>
Milwaukee School of Engineering<br>
Minia University<br>
Mississippi State University<br>
Missouri University of Science and Technology<br>
Mohammad Ali Jinnah University<br>
Mohammed V University in Rabat<br>
Monash University<br>
Multimedia University<br>
Murdoch University<br>
Nanjing University<br>
Nanchang Hangkong University<br>
Nanjing Medical University<br>
Nanjing University<br>
National Chung Hsing University<br>
National Institute of Technical Teachers Training & Research<br>
National Institute of Technology Trichy<br>
National Institute of Technology, Warangal<br>
National Sun Yat-sen University<br>
National Taichung University of Science and Technology<br>
National Taiwan University<br>
National Technical University of Athens<br>
National Technical University of Ukraine<br>
National United University<br>
National University of Sciences and Technology<br>
National University of Singapore<br>
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Newman University<br>
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Ohio University<br>
Pakuan University<br>
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Pennsylvania State University<br>
Pohang University of Science and Technology<br>
Politechnika Białostocka<br>
Politecnico di Milano<br>
Politeknik Negeri Semarang<br>
Pomona College<br>
Pontificia Universidad Católica de Chile<br>
Pontificia Universidad Católica del Perú<br>
Portland State University<br>
Punjabi University<br>
Purdue University<br>
Purdue University Northwest<br>
Quaid-e-Azam University<br>
Queen Mary University of London<br>
Queen's University<br>
Radboud Universiteit<br>
Radboud University<br>
Rajiv Gandhi Institute of Petroleum Technology<br>
Rensselaer Polytechnic Institute<br>
Rowan University<br>
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RVS Institute of Management Studies and Research<br>
RWTH Aachen University<br>
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Santa Clara University<br>
Sapienza Università di Roma<br>
Seoul National University<br>
Seoul National University of Science and Technology<br>
Shanghai Jiao Tong University<br>
Shanghai University of Electric Power<br>
Shanghai University of Finance and Economics<br>
Shantilal Shah Engineering College<br>
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Simon Fraser University<br>
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Stellenbosch University<br>
Stevens Institute of Technology<br>
Sungkyunkwan University<br>
Technion - Israel Institute of Technology<br>
Technische Universität Berlin<br>
Technische Universität München<br>
Technische Universiteit Delft<br>
Tecnológico de Monterrey, Campus Guadalajara<br>
Tekirdağ Namık Kemal Üniversitesi<br>
Télécom Paris<br>
Telkom University<br>
Texas A&M University<br>
Thapar Institute of Engineering and Technology<br>
Tsinghua University<br>
Tufts University<br>
Umeå University<br>
Universidad Carlos III de Madrid<br>
Universidad de Ibagué<br>
Universidad de Ingeniería y Tecnología - UTEC<br>
Universidad de Salamanca<br>
Universidad de Zaragoza<br>
Universidad del Norte, Colombia<br>
Universidad Icesi<br>
Universidad Militar Nueva Granada<br>
Universidad Nacional Agraria La Molina<br>
Universidad Nacional Autónoma de México<br>
Universidad Nacional de Colombia Sede Manizales<br>
Universidad Nacional de Tierra del Fuego<br>
Universidad Politécnica de Chiapas<br>
Universidad Politécnica de Valencia<br>
Universidad Politécnica Salesiana, Cuenca<br>
Universidad Rafael Landivar<br>
Universidad Rey Juan Carlos<br>
Universidad San Francisco de Quito<br>
Universidad Tecnológica de Pereira<br>
Universidad Tecnológica Nacional<br>
Universidade Católica de Brasília<br>
Universidade Estadual de Campinas<br>
Universidade Federal de Goiás<br>
Universidade Federal de Minas Gerais<br>
Universidade Federal de Ouro Preto<br>
Universidade Federal de Pernambuco<br>
Universidade Federal de São Carlos<br>
Universidade Federal de Viçosa<br>
Universidade Federal do Pampa<br>
Universidade Federal do Rio Grande<br>
Universidade NOVA de Lisboa<br>
Universidade Presbiteriana Mackenzie<br>
Universidade Tecnológica Federal do Paraná<br>
Università Cattolica del Sacro Cuore<br>
Università degli Studi di Bari Aldo Moro<br>
Università degli Studi di Brescia<br>
Università degli Studi di Catania<br>
Università degli Studi di Padova<br>
Universitas Andalas, Padang<br>
Universitas Indonesia<br>
Universitas Negeri Yogyakarta<br>
Universitas Udayana<br>
Universität Bremen<br>
Universitat de Barcelona<br>
Universitat de València<br>
Universität Heidelberg<br>
Universität Leipzig<br>
Universitat Politècnica de Catalunya<br>
Universitatea Babeș-Bolyai<br>
Universitatea de Vest din Timișoara<br>
Université Abderrahmane Mira de Béjaïa<br>
Université Clermont Auvergne<br>
Université Côte d'Azur<br>
Université de Caen Normandie<br>
Université de Rouen Normandie<br>
Université de technologie de Compiègne<br>
Université Paris-Saclay<br>
Université Toulouse 1 Capitole<br>
University of Akron<br>
University of Alabama in Huntsville<br>
University of Allahabad<br>
University of Applied Sciences Würzburg-Schweinfurt<br>
University of Arkansas<br>
University of Augsburg<br>
University of Baghdad<br>
University of Bath<br>
University of Bordj Bou Arreridj<br>
University of British Columbia<br>
University of California, Berkeley<br>
University of California, Irvine<br>
University of California, Los Angeles<br>
University of California, San Diego<br>
University of California, Santa Barbara<br>
University of California, Santa Cruz<br>
University of Cambridge<br>
University of Canberra<br>
University of Catania<br>
University of Cincinnati<br>
University of Colorado Boulder<br>
University of Connecticut<br>
University of Copenhagen<br>
University of Derby<br>
University of Florida<br>
University of Genoa<br>
University of Ghana<br>
University of Groningen<br>
University of Hamburg<br>
University of Houston<br>
University of Hull<br>
University of Iceland<br>
University of Idaho<br>
University of Illinois at Urbana-Champaign<br>
University of International Business and Economics<br>
University of Klagenfurt<br>
University of Liège<br>
University of Louisiana at Lafayette<br>
University of Maryland<br>
University of Maryland Baltimore County<br>
University of Massachusetts Lowell<br>
University of Michigan<br>
University of Michigan Dearborn<br>
University of Milano-Bicocca<br>
University of Minnesota, Twin Cities<br>
University of Moratuwa<br>
University of Nebraska Omaha<br>
University of New Hampshire<br>
University of Newcastle<br>
University of North Carolina at Chapel Hill<br>
University of North Texas<br>
University of Northern Philippines<br>
University of Nottingham<br>
University of Oslo<br>
University of Pennsylvania<br>
University of Pittsburgh<br>
University of Rostock<br>
University of São Paulo<br>
University of Science and Technology of China<br>
University of Southern California<br>
University of Southern Maine<br>
University of St Andrews<br>
University of St. Thomas<br>
University of Suffolk<br>
University of Sydney<br>
University of Szeged<br>
University of Technology Sydney<br>
University of Tehran<br>
University of Texas at Austin<br>
University of Texas at Dallas<br>
University of Texas Rio Grande Valley<br>
University of Udine<br>
University of Warsaw<br>
University of Washington<br>
University of Waterloo<br>
University of Wisconsin Madison<br>
Univerzita Komenského v Bratislave<br>
Uniwersytet Jagielloński<br>
Vardhaman College of Engineering<br>
Vardhman Mahaveer Open University<br>
Vietnamese-German University<br>
Vignana Jyothi Institute Of Management<br>
Vilnius University<br>
Wageningen University<br>
West Virginia University<br>
Western University<br>
Wichita State University<br>
Xavier University Bhubaneswar<br>
Xi'an Jiaotong Liverpool University<br>
Xiamen University<br>
Xianning Vocational Technical College<br>
Yale University<br>
Yeshiva University<br>
Yıldız Teknik Üniversitesi<br>
Yonsei University<br>
Yunnan University<br>
Zhejiang University
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<h3>BibTeX entry for citing the book</h3>
<br>
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<div class="highlight-python notranslate">
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<tt>
<pre>@book{zhang2023dive,
title={Dive into Deep Learning},
author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
publisher={Cambridge University Press},
note={\url{https://D2L.ai}},
year={2023}
}</pre>
</tt>
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<h2 class="toc"> Table of contents </h2>
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<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="chapter_preface/index.html">Preface</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_installation/index.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter_notation/index.html">Notation</a></li>
</ul>
</div>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="chapter_introduction/index.html">1. Introduction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#a-motivating-example">1.1. A Motivating Example</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#key-components">1.2. Key Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#kinds-of-machine-learning-problems">1.3. Kinds of Machine Learning Problems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#roots">1.4. Roots</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#the-road-to-deep-learning">1.5. The Road to Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#success-stories">1.6. Success Stories</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#the-essence-of-deep-learning">1.7. The Essence of Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#summary">1.8. Summary</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_introduction/index.html#exercises">1.9. Exercises</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_preliminaries/index.html">2. Preliminaries</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/ndarray.html">2.1. Data Manipulation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/pandas.html">2.2. Data Preprocessing</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/linear-algebra.html">2.3. Linear Algebra</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/calculus.html">2.4. Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/autograd.html">2.5. Automatic Differentiation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/probability.html">2.6. Probability and Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_preliminaries/lookup-api.html">2.7. Documentation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-regression/index.html">3. Linear Neural Networks for Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression.html">3.1. Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/oo-design.html">3.2. Object-Oriented Design for Implementation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/synthetic-regression-data.html">3.3. Synthetic Regression Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-scratch.html">3.4. Linear Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/linear-regression-concise.html">3.5. Concise Implementation of Linear Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/generalization.html">3.6. Generalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-regression/weight-decay.html">3.7. Weight Decay</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_linear-classification/index.html">4. Linear Neural Networks for Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression.html">4.1. Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/image-classification-dataset.html">4.2. The Image Classification Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/classification.html">4.3. The Base Classification Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-scratch.html">4.4. Softmax Regression Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/softmax-regression-concise.html">4.5. Concise Implementation of Softmax Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/generalization-classification.html">4.6. Generalization in Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_linear-classification/environment-and-distribution-shift.html">4.7. Environment and Distribution Shift</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_multilayer-perceptrons/index.html">5. Multilayer Perceptrons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp.html">5.1. Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/mlp-implementation.html">5.2. Implementation of Multilayer Perceptrons</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/backprop.html">5.3. Forward Propagation, Backward Propagation, and Computational Graphs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/numerical-stability-and-init.html">5.4. Numerical Stability and Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/generalization-deep.html">5.5. Generalization in Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/dropout.html">5.6. Dropout</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_multilayer-perceptrons/kaggle-house-price.html">5.7. Predicting House Prices on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_builders-guide/index.html">6. Builders’ Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/model-construction.html">6.1. Layers and Modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/parameters.html">6.2. Parameter Management</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/init-param.html">6.3. Parameter Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/lazy-init.html">6.4. Lazy Initialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/custom-layer.html">6.5. Custom Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/read-write.html">6.6. File I/O</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_builders-guide/use-gpu.html">6.7. GPUs</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-neural-networks/index.html">7. Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/why-conv.html">7.1. From Fully Connected Layers to Convolutions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/conv-layer.html">7.2. Convolutions for Images</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/padding-and-strides.html">7.3. Padding and Stride</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/channels.html">7.4. Multiple Input and Multiple Output Channels</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/pooling.html">7.5. Pooling</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-neural-networks/lenet.html">7.6. Convolutional Neural Networks (LeNet)</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_convolutional-modern/index.html">8. Modern Convolutional Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/alexnet.html">8.1. Deep Convolutional Neural Networks (AlexNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/vgg.html">8.2. Networks Using Blocks (VGG)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/nin.html">8.3. Network in Network (NiN)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/googlenet.html">8.4. Multi-Branch Networks (GoogLeNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/batch-norm.html">8.5. Batch Normalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/resnet.html">8.6. Residual Networks (ResNet) and ResNeXt</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/densenet.html">8.7. Densely Connected Networks (DenseNet)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_convolutional-modern/cnn-design.html">8.8. Designing Convolution Network Architectures</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-neural-networks/index.html">9. Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/sequence.html">9.1. Working with Sequences</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/text-sequence.html">9.2. Converting Raw Text into Sequence Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/language-model.html">9.3. Language Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn.html">9.4. Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-scratch.html">9.5. Recurrent Neural Network Implementation from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/rnn-concise.html">9.6. Concise Implementation of Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-neural-networks/bptt.html">9.7. Backpropagation Through Time</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recurrent-modern/index.html">10. Modern Recurrent Neural Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/lstm.html">10.1. Long Short-Term Memory (LSTM)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/gru.html">10.2. Gated Recurrent Units (GRU)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/deep-rnn.html">10.3. Deep Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/bi-rnn.html">10.4. Bidirectional Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/machine-translation-and-dataset.html">10.5. Machine Translation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/encoder-decoder.html">10.6. The Encoder–Decoder Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/seq2seq.html">10.7. Sequence-to-Sequence Learning for Machine Translation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recurrent-modern/beam-search.html">10.8. Beam Search</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/index.html">11. Attention Mechanisms and Transformers</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/queries-keys-values.html">11.1. Queries, Keys, and Values</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-pooling.html">11.2. Attention Pooling by Similarity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/attention-scoring-functions.html">11.3. Attention Scoring Functions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/bahdanau-attention.html">11.4. The Bahdanau Attention Mechanism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/multihead-attention.html">11.5. Multi-Head Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/self-attention-and-positional-encoding.html">11.6. Self-Attention and Positional Encoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/transformer.html">11.7. The Transformer Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/vision-transformer.html">11.8. Transformers for Vision</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_attention-mechanisms-and-transformers/large-pretraining-transformers.html">11.9. Large-Scale Pretraining with Transformers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_optimization/index.html">12. Optimization Algorithms</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/optimization-intro.html">12.1. Optimization and Deep Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/convexity.html">12.2. Convexity</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/gd.html">12.3. Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/sgd.html">12.4. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/minibatch-sgd.html">12.5. Minibatch Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/momentum.html">12.6. Momentum</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adagrad.html">12.7. Adagrad</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/rmsprop.html">12.8. RMSProp</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adadelta.html">12.9. Adadelta</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/adam.html">12.10. Adam</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_optimization/lr-scheduler.html">12.11. Learning Rate Scheduling</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computational-performance/index.html">13. Computational Performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hybridize.html">13.1. Compilers and Interpreters</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/async-computation.html">13.2. Asynchronous Computation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/auto-parallelism.html">13.3. Automatic Parallelism</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/hardware.html">13.4. Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus.html">13.5. Training on Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/multiple-gpus-concise.html">13.6. Concise Implementation for Multiple GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computational-performance/parameterserver.html">13.7. Parameter Servers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_computer-vision/index.html">14. Computer Vision</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/image-augmentation.html">14.1. Image Augmentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fine-tuning.html">14.2. Fine-Tuning</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/bounding-box.html">14.3. Object Detection and Bounding Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/anchor.html">14.4. Anchor Boxes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/multiscale-object-detection.html">14.5. Multiscale Object Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/object-detection-dataset.html">14.6. The Object Detection Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/ssd.html">14.7. Single Shot Multibox Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/rcnn.html">14.8. Region-based CNNs (R-CNNs)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/semantic-segmentation-and-dataset.html">14.9. Semantic Segmentation and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/transposed-conv.html">14.10. Transposed Convolution</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/fcn.html">14.11. Fully Convolutional Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/neural-style.html">14.12. Neural Style Transfer</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-cifar10.html">14.13. Image Classification (CIFAR-10) on Kaggle</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_computer-vision/kaggle-dog.html">14.14. Dog Breed Identification (ImageNet Dogs) on Kaggle</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-pretraining/index.html">15. Natural Language Processing: Pretraining</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec.html">15.1. Word Embedding (word2vec)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/approx-training.html">15.2. Approximate Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word-embedding-dataset.html">15.3. The Dataset for Pretraining Word Embeddings</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/word2vec-pretraining.html">15.4. Pretraining word2vec</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/glove.html">15.5. Word Embedding with Global Vectors (GloVe)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/subword-embedding.html">15.6. Subword Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/similarity-analogy.html">15.7. Word Similarity and Analogy</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert.html">15.8. Bidirectional Encoder Representations from Transformers (BERT)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-dataset.html">15.9. The Dataset for Pretraining BERT</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-pretraining/bert-pretraining.html">15.10. Pretraining BERT</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_natural-language-processing-applications/index.html">16. Natural Language Processing: Applications</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-and-dataset.html">16.1. Sentiment Analysis and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-rnn.html">16.2. Sentiment Analysis: Using Recurrent Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/sentiment-analysis-cnn.html">16.3. Sentiment Analysis: Using Convolutional Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html">16.4. Natural Language Inference and the Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-attention.html">16.5. Natural Language Inference: Using Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/finetuning-bert.html">16.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_natural-language-processing-applications/natural-language-inference-bert.html">16.7. Natural Language Inference: Fine-Tuning BERT</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_reinforcement-learning/index.html">17. Reinforcement Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/mdp.html">17.1. Markov Decision Process (MDP)</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/value-iter.html">17.2. Value Iteration</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_reinforcement-learning/qlearning.html">17.3. Q-Learning</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_gaussian-processes/index.html">18. Gaussian Processes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-intro.html">18.1. Introduction to Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-priors.html">18.2. Gaussian Process Priors</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_gaussian-processes/gp-inference.html">18.3. Gaussian Process Inference</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_hyperparameter-optimization/index.html">19. Hyperparameter Optimization</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-intro.html">19.1. What Is Hyperparameter Optimization?</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/hyperopt-api.html">19.2. Hyperparameter Optimization API</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/rs-async.html">19.3. Asynchronous Random Search</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-intro.html">19.4. Multi-Fidelity Hyperparameter Optimization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_hyperparameter-optimization/sh-async.html">19.5. Asynchronous Successive Halving</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_generative-adversarial-networks/index.html">20. Generative Adversarial Networks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/gan.html">20.1. Generative Adversarial Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_generative-adversarial-networks/dcgan.html">20.2. Deep Convolutional Generative Adversarial Networks</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_recommender-systems/index.html">21. Recommender Systems</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/recsys-intro.html">21.1. Overview of Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/movielens.html">21.2. The MovieLens Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/mf.html">21.3. Matrix Factorization</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/autorec.html">21.4. AutoRec: Rating Prediction with Autoencoders</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ranking.html">21.5. Personalized Ranking for Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/neumf.html">21.6. Neural Collaborative Filtering for Personalized Ranking</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/seqrec.html">21.7. Sequence-Aware Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/ctr.html">21.8. Feature-Rich Recommender Systems</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/fm.html">21.9. Factorization Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_recommender-systems/deepfm.html">21.10. Deep Factorization Machines</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/index.html">22. Appendix: Mathematics for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/geometry-linear-algebraic-ops.html">22.1. Geometry and Linear Algebraic Operations</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/eigendecomposition.html">22.2. Eigendecompositions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/single-variable-calculus.html">22.3. Single Variable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/multivariable-calculus.html">22.4. Multivariable Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/integral-calculus.html">22.5. Integral Calculus</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/random-variables.html">22.6. Random Variables</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/maximum-likelihood.html">22.7. Maximum Likelihood</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/distributions.html">22.8. Distributions</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/naive-bayes.html">22.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/statistics.html">22.10. Statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-mathematics-for-deep-learning/information-theory.html">22.11. Information Theory</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/index.html">23. Appendix: Tools for Deep Learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/jupyter.html">23.1. Using Jupyter Notebooks</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/sagemaker.html">23.2. Using Amazon SageMaker</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/aws.html">23.3. Using AWS EC2 Instances</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/colab.html">23.4. Using Google Colab</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/selecting-servers-gpus.html">23.5. Selecting Servers and GPUs</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/contributing.html">23.6. Contributing to This Book</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/utils.html">23.7. Utility Functions and Classes</a></li>
<li class="toctree-l2"><a class="reference internal" href="chapter_appendix-tools-for-deep-learning/d2l.html">23.8. The <code class="docutils literal notranslate"><span class="pre">d2l</span></code> API Document</a></li>
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| /chapter_linear-classification/image-classification-dataset.html |
4.2. The Image Classification Dataset
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| /chapter_linear-classification/classification.html |
4.3. The Base Classification Model
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| /chapter_linear-classification/softmax-regression-scratch.html |
4.4. Softmax Regression Implementation from Scratch
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4.5. Concise Implementation of Softmax Regression
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| /chapter_linear-classification/generalization-classification.html |
4.6. Generalization in Classification
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| /chapter_linear-classification/environment-and-distribution-shift.html |
4.7. Environment and Distribution Shift
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| /chapter_multilayer-perceptrons/index.html |
5. Multilayer Perceptrons
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| /chapter_multilayer-perceptrons/mlp.html |
5.1. Multilayer Perceptrons
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| /chapter_multilayer-perceptrons/mlp-implementation.html |
5.2. Implementation of Multilayer Perceptrons
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| /chapter_multilayer-perceptrons/backprop.html |
5.3. Forward Propagation, Backward Propagation, and Computational Graphs
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| /chapter_multilayer-perceptrons/numerical-stability-and-init.html |
5.4. Numerical Stability and Initialization
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| /chapter_multilayer-perceptrons/generalization-deep.html |
5.5. Generalization in Deep Learning
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| /chapter_multilayer-perceptrons/dropout.html |
5.6. Dropout
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| /chapter_multilayer-perceptrons/kaggle-house-price.html |
5.7. Predicting House Prices on Kaggle
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| /chapter_builders-guide/index.html |
6. Builders’ Guide
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| /chapter_builders-guide/model-construction.html |
6.1. Layers and Modules
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| /chapter_builders-guide/parameters.html |
6.2. Parameter Management
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| /chapter_convolutional-neural-networks/index.html |
7. Convolutional Neural Networks
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| /chapter_convolutional-neural-networks/why-conv.html |
7.1. From Fully Connected Layers to Convolutions
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7.3. Padding and Stride
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7.5. Pooling
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7.6. Convolutional Neural Networks (LeNet)
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| /chapter_convolutional-modern/index.html |
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| /chapter_convolutional-modern/densenet.html |
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| /chapter_convolutional-modern/cnn-design.html |
8.8. Designing Convolution Network Architectures
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| /chapter_recurrent-neural-networks/index.html |
9. Recurrent Neural Networks
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| /chapter_recurrent-neural-networks/sequence.html |
9.1. Working with Sequences
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| /chapter_recurrent-neural-networks/text-sequence.html |
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| /chapter_recurrent-neural-networks/language-model.html |
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| /chapter_recurrent-neural-networks/rnn.html |
9.4. Recurrent Neural Networks
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| /chapter_recurrent-neural-networks/rnn-scratch.html |
9.5. Recurrent Neural Network Implementation from Scratch
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| /chapter_recurrent-neural-networks/rnn-concise.html |
9.6. Concise Implementation of Recurrent Neural Networks
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| /chapter_recurrent-neural-networks/bptt.html |
9.7. Backpropagation Through Time
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| /chapter_recurrent-modern/index.html |
10. Modern Recurrent Neural Networks
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| /chapter_recurrent-modern/lstm.html |
10.1. Long Short-Term Memory (LSTM)
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10.2. Gated Recurrent Units (GRU)
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10.3. Deep Recurrent Neural Networks
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10.4. Bidirectional Recurrent Neural Networks
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10.5. Machine Translation and the Dataset
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10.6. The Encoder–Decoder Architecture
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| /chapter_recurrent-modern/seq2seq.html |
10.7. Sequence-to-Sequence Learning for Machine Translation
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10.8. Beam Search
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| /chapter_attention-mechanisms-and-transformers/queries-keys-values.html |
11.1. Queries, Keys, and Values
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11.7. The Transformer Architecture
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11.8. Transformers for Vision
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11.9. Large-Scale Pretraining with Transformers
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| /chapter_optimization/index.html |
12. Optimization Algorithms
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| /chapter_optimization/optimization-intro.html |
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12.5. Minibatch Stochastic Gradient Descent
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12.6. Momentum
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