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# Companion notebooks for Deep Learning with Python
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/fchollet/deep-learning-with-python-notebooks) · [上游 README](https://github.com/fchollet/deep-learning-with-python-notebooks/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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This repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python, third edition (2025)](https://www.manning.com/books/deep-learning-with-python-third-edition?a_aid=keras&a_bid=76564dff)
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by Francois Chollet and Matthew Watson. In addition, you will also find the legacy notebooks for the [second edition (2021)](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff)
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and the [first edition (2017)](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff).
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# 《Python 深度学习》配套 Jupyter 笔记本
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For readability, these notebooks only contain runnable code blocks and section titles, and omit everything else in the book: text paragraphs, figures, and pseudocode.
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**If you want to be able to follow what's going on, I recommend reading the notebooks side by side with your copy of the book.**
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本仓库包含实现了书中代码示例的 Jupyter 笔记本，对应书籍 [《Python 深度学习》第三版（2025）](https://www.manning.com/books/deep-learning-with-python-third-edition?a_aid=keras&a_bid=76564dff)
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，作者为 Francois Chollet 和 Matthew Watson。此外，你还可以找到 [第二版（2021）](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff)
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以及 [第一版（2017）](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff).
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的旧版笔记本。
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## Running the code
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为提高可读性，这些笔记本仅包含可运行的代码块和章节标题，省略了书中其余内容：文字段落、插图和伪代码。
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**若想跟上讲解脉络，建议你将笔记本与手中的纸质书或电子书对照阅读。**
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We recommend running these notebooks on [Colab](https://colab.google), which
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provides a hosted runtime with all the dependencies you will need. You can also,
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run these notebooks locally, either by setting up your own Jupyter environment,
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or using Colab's instructions for
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[running locally](https://research.google.com/colaboratory/local-runtimes.html).
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## 运行代码
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By default, all notebooks will run on Colab's free tier GPU runtime, which
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is sufficient to run all code in this book. Chapter 8-18 chapters will benefit
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from a faster GPU if you have a Colab Pro subscription. You can change your
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runtime type using **Runtime -> Change runtime type** in Colab's dropdown menus.
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我们建议在 [Colab](https://colab.google), 上运行这些笔记本，它
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提供托管运行时，并已预装你所需的全部依赖。你也可以在本地运行这些笔记本：自行搭建 Jupyter 环境，或按照 Colab 的
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[本地运行](https://research.google.com/colaboratory/local-runtimes.html).
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说明操作。
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## Choosing a backend
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默认情况下，所有笔记本都会在 Colab 的免费 GPU 运行时上执行，足以运行本书全部代码。若你订阅了 Colab Pro，第 8–18 章在更快的 GPU 上会运行得更顺畅。可在 Colab 下拉菜单中使用 **Runtime -> Change runtime type** 更改运行时类型。
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The code for third edition is written using Keras 3. As such, it can be run with
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JAX, TensorFlow or PyTorch as a backend. To set the backend, update the backend
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in the cell at the top of the colab that looks like this:
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## 选择后端
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第三版代码使用 Keras 3 编写，因此可选用 JAX、TensorFlow 或 PyTorch 作为后端。要设置后端，请修改 Colab 顶部类似下面示例的单元格中的 backend 设置：
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```python
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import os
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os.environ["KERAS_BACKEND"] = "jax"
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```
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This must be done only once per session before importing Keras. If you are
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in the middle running a notebook, you will need to restart the notebook session
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and rerun all relevant notebook cells. This can be done in using
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**Runtime -> Restart Session** in Colab's dropdown menus.
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每个会话在导入 Keras 之前只需执行一次。若你正在运行笔记本中途切换后端，需要重启笔记本会话并重新运行所有相关单元格。可在 Colab 下拉菜单中使用 **Runtime -> Restart Session** 完成重启。
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## Using Kaggle data
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## 使用 Kaggle 数据
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This book uses datasets and model weights provided by Kaggle, an online Machine
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Learning community and platform. You will need to create a Kaggle login to run
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Kaggle code in this book; instructions are given in Chapter 8.
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本书使用由 Kaggle——一个在线机器学习（Machine Learning）社区与平台——提供的数据集和模型权重。运行书中的 Kaggle 相关代码前，你需要创建 Kaggle 账号；具体说明见第 8 章。
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For chapters that need Kaggle data, you can login to Kaggle once per session
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when you hit the notebook cell with `kagglehub.login()`. Alternately,
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you can set up your Kaggle login information once as Colab secrets:
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对于需要 Kaggle 数据的章节，当你运行到包含 `kagglehub.login()` 的笔记本单元格时，可在每个会话中登录一次 Kaggle。或者，你也可以将 Kaggle 登录信息一次性配置为 Colab secrets：
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* Go to https://www.kaggle.com/ and sign in.
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* Go to https://www.kaggle.com/settings and generate a Kaggle API key.
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* Open the secrets tab in Colab by clicking the key icon on the left.
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* Add two secrets, `KAGGLE_USERNAME` and `KAGGLE_KEY` with the username and key
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you just created.
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* 访问 https://www.kaggle.com/ 并登录。
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* 访问 https://www.kaggle.com/settings 并生成 Kaggle API 密钥。
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* 点击左侧钥匙图标，打开 Colab 的 secrets 标签页。
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* 添加两个 secrets：`KAGGLE_USERNAME` 和 `KAGGLE_KEY`，分别填入你刚创建的用户名与密钥。
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Following this approach you will only need to copy your Kaggle secret key once,
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though you will need to allow each notebook to access your secrets when running
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the relevant Kaggle code.
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按这种方式，你只需复制一次 Kaggle 密钥，但在运行相关 Kaggle 代码时，仍需允许每个笔记本访问你的 secrets。
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## Table of contents
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## 目录
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* [Chapter 2: The mathematical building blocks of neural networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter02_mathematical-building-blocks.ipynb)
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* [Chapter 3: Introduction to TensorFlow, PyTorch, JAX, and Keras](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter03_introduction-to-ml-frameworks.ipynb)
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* [Chapter 4: Classification and regression](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_classification-and-regression.ipynb)
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* [Chapter 5: Fundamentals of machine learning](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter05_fundamentals-of-ml.ipynb)
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* [Chapter 7: A deep dive on Keras](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_deep-dive-keras.ipynb)
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* [Chapter 8: Image Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_image-classification.ipynb)
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* [Chapter 9: Convnet architecture patterns](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_convnet-architecture-patterns.ipynb)
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* [Chapter 10: Interpreting what ConvNets learn](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter10_interpreting-what-convnets-learn.ipynb)
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* [Chapter 11: Image Segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_image-segmentation.ipynb)
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* [Chapter 12: Object Detection](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_object-detection.ipynb)
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* [Chapter 13: Timeseries Forecasting](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_timeseries-forecasting.ipynb)
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* [Chapter 14: Text Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_text-classification.ipynb)
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* [Chapter 15: Language Models and the Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter15_language-models-and-the-transformer.ipynb)
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* [Chapter 16: Text Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter16_text-generation.ipynb)
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* [Chapter 17: Image Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter17_image-generation.ipynb)
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* [Chapter 18: Best practices for the real world](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter18_best-practices-for-the-real-world.ipynb)
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* [第 2 章：神经网络的数学构建块](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter02_mathematical-building-blocks.ipynb)
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* [第 3 章：TensorFlow、PyTorch、JAX 与 Keras 简介](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter03_introduction-to-ml-frameworks.ipynb)
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* [第 4 章：分类与回归](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_classification-and-regression.ipynb)
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* [第 5 章：机器学习基础](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter05_fundamentals-of-ml.ipynb)
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* [第 7 章：深入 Keras](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_deep-dive-keras.ipynb)
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* [第 8 章：图像分类](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_image-classification.ipynb)
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* [第 9 章：卷积网络（ConvNet）架构模式](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_convnet-architecture-patterns.ipynb)
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* [第 10 章：解读卷积网络学到了什么](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter10_interpreting-what-convnets-learn.ipynb)
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* [第 11 章：图像分割](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_image-segmentation.ipynb)
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* [第 12 章：目标检测](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_object-detection.ipynb)
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* [第 13 章：时间序列预测](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_timeseries-forecasting.ipynb)
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* [第 14 章：文本分类](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_text-classification.ipynb)
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* [第 15 章：语言模型与 Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter15_language-models-and-the-transformer.ipynb)
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* [第 16 章：文本生成](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter16_text-generation.ipynb)
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* [第 17 章：图像生成](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter17_image-generation.ipynb)
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* [第 18 章：真实场景最佳实践](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter18_best-practices-for-the-real-world.ipynb)
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