[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70859":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},70859,"deep-learning-with-python-notebooks","fchollet\u002Fdeep-learning-with-python-notebooks","fchollet","Jupyter notebooks for the code samples of the book \"Deep Learning with Python\"","",null,"Jupyter Notebook",20130,9051,646,178,0,6,49,18,45,"MIT License",false,"master",true,[],"2026-06-12 02:02:44","# Companion notebooks for Deep Learning with Python\n\nThis repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python, third edition (2025)](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-python-third-edition?a_aid=keras&a_bid=76564dff)\nby Francois Chollet and Matthew Watson. In addition, you will also find the legacy notebooks for the [second edition (2021)](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff)\nand the [first edition (2017)](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-python?a_aid=keras&a_bid=76564dff).\n\nFor readability, these notebooks only contain runnable code blocks and section titles, and omit everything else in the book: text paragraphs, figures, and pseudocode.\n**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.**\n\n## Running the code\n\nWe recommend running these notebooks on [Colab](https:\u002F\u002Fcolab.google), which\nprovides a hosted runtime with all the dependencies you will need. You can also,\nrun these notebooks locally, either by setting up your own Jupyter environment,\nor using Colab's instructions for\n[running locally](https:\u002F\u002Fresearch.google.com\u002Fcolaboratory\u002Flocal-runtimes.html).\n\nBy default, all notebooks will run on Colab's free tier GPU runtime, which\nis sufficient to run all code in this book. Chapter 8-18 chapters will benefit\nfrom a faster GPU if you have a Colab Pro subscription. You can change your\nruntime type using **Runtime -> Change runtime type** in Colab's dropdown menus.\n\n## Choosing a backend\n\nThe code for third edition is written using Keras 3. As such, it can be run with\nJAX, TensorFlow or PyTorch as a backend. To set the backend, update the backend\nin the cell at the top of the colab that looks like this:\n\n```python\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n```\n\nThis must be done only once per session before importing Keras. If you are\nin the middle running a notebook, you will need to restart the notebook session\nand rerun all relevant notebook cells. This can be done in using\n**Runtime -> Restart Session** in Colab's dropdown menus.\n\n## Using Kaggle data\n\nThis book uses datasets and model weights provided by Kaggle, an online Machine\nLearning community and platform. You will need to create a Kaggle login to run\nKaggle code in this book; instructions are given in Chapter 8.\n\nFor chapters that need Kaggle data, you can login to Kaggle once per session\nwhen you hit the notebook cell with `kagglehub.login()`. Alternately,\nyou can set up your Kaggle login information once as Colab secrets:\n\n * Go to https:\u002F\u002Fwww.kaggle.com\u002F and sign in.\n * Go to https:\u002F\u002Fwww.kaggle.com\u002Fsettings and generate a Kaggle API key.\n * Open the secrets tab in Colab by clicking the key icon on the left.\n * Add two secrets, `KAGGLE_USERNAME` and `KAGGLE_KEY` with the username and key\n   you just created.\n\nFollowing this approach you will only need to copy your Kaggle secret key once,\nthough you will need to allow each notebook to access your secrets when running\nthe relevant Kaggle code.\n\n## Table of contents\n\n* [Chapter 2: The mathematical building blocks of neural networks](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter02_mathematical-building-blocks.ipynb)\n* [Chapter 3: Introduction to TensorFlow, PyTorch, JAX, and Keras](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter03_introduction-to-ml-frameworks.ipynb)\n* [Chapter 4: Classification and regression](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter04_classification-and-regression.ipynb)\n* [Chapter 5: Fundamentals of machine learning](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter05_fundamentals-of-ml.ipynb)\n* [Chapter 7: A deep dive on Keras](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter07_deep-dive-keras.ipynb)\n* [Chapter 8: Image Classification](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter08_image-classification.ipynb)\n* [Chapter 9: Convnet architecture patterns](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter09_convnet-architecture-patterns.ipynb)\n* [Chapter 10: Interpreting what ConvNets learn](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter10_interpreting-what-convnets-learn.ipynb)\n* [Chapter 11: Image Segmentation](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter11_image-segmentation.ipynb)\n* [Chapter 12: Object Detection](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter12_object-detection.ipynb)\n* [Chapter 13: Timeseries Forecasting](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter13_timeseries-forecasting.ipynb)\n* [Chapter 14: Text Classification](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter14_text-classification.ipynb)\n* [Chapter 15: Language Models and the Transformer](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter15_language-models-and-the-transformer.ipynb)\n* [Chapter 16: Text Generation](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter16_text-generation.ipynb)\n* [Chapter 17: Image Generation](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter17_image-generation.ipynb)\n* [Chapter 18: Best practices for the real world](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffchollet\u002Fdeep-learning-with-python-notebooks\u002Fblob\u002Fmaster\u002Fchapter18_best-practices-for-the-real-world.ipynb)\n","该项目提供了《Python深度学习》一书的代码示例Jupyter笔记本。核心功能包括使用Keras 3实现书中介绍的各种深度学习模型，并支持JAX、TensorFlow或PyTorch作为后端。技术特点有：所有代码块可直接运行，推荐在Google Colab上执行以利用其免费GPU资源；对于需要更多计算能力的部分章节，建议使用Colab Pro订阅提供的更快GPU。此外，项目还指导用户如何通过Kaggle获取数据集和预训练模型权重。适合场景包括深度学习入门者自学、教育机构教学以及研究人员快速验证书中概念。",2,"2026-06-11 03:34:37","high_star"]