{"id":13426984,"url":"https://github.com/fchollet/deep-learning-with-python-notebooks","last_synced_at":"2025-05-13T15:08:24.062Z","repository":{"id":37390508,"uuid":"102523304","full_name":"fchollet/deep-learning-with-python-notebooks","owner":"fchollet","description":"Jupyter notebooks for the code samples of the book \"Deep Learning with Python\"","archived":false,"fork":false,"pushed_at":"2025-05-01T03:21:00.000Z","size":7119,"stargazers_count":19157,"open_issues_count":181,"forks_count":8817,"subscribers_count":655,"default_branch":"master","last_synced_at":"2025-05-06T14:56:47.784Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","readme":"# Companion Jupyter notebooks for the book \"Deep Learning with Python\"\n\nThis repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python, 2nd Edition (Manning Publications)](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras\u0026a_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\nThese notebooks use TensorFlow 2.6.\n\n## Table of contents\n\n* [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)\n* [Chapter 3: Introduction to Keras and TensorFlow](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter03_introduction-to-keras-and-tf.ipynb)\n* [Chapter 4: Getting started with neural networks: classification and regression](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_getting-started-with-neural-networks.ipynb)\n* [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)\n* [Chapter 7: Working with Keras: a deep dive](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_working-with-keras.ipynb)\n* [Chapter 8: Introduction to deep learning for computer vision](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_intro-to-dl-for-computer-vision.ipynb)\n* Chapter 9: Advanced deep learning for computer vision\n    - [Part 1: Image segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part01_image-segmentation.ipynb)\n    - [Part 2: Modern convnet architecture patterns](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part02_modern-convnet-architecture-patterns.ipynb)\n    - [Part 3: Interpreting what convnets learn](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part03_interpreting-what-convnets-learn.ipynb)\n* [Chapter 10: Deep learning for timeseries](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter10_dl-for-timeseries.ipynb)\n* Chapter 11: Deep learning for text\n    - [Part 1: Introduction](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part01_introduction.ipynb)\n    - [Part 2: Sequence models](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part02_sequence-models.ipynb)\n    - [Part 3: Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part03_transformer.ipynb)\n    - [Part 4: Sequence-to-sequence learning](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part04_sequence-to-sequence-learning.ipynb)\n* Chapter 12: Generative deep learning\n    - [Part 1: Text generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part01_text-generation.ipynb)\n    - [Part 2: Deep Dream](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part02_deep-dream.ipynb)\n    - [Part 3: Neural style transfer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part03_neural-style-transfer.ipynb)\n    - [Part 4: Variational autoencoders](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part04_variational-autoencoders.ipynb)\n    - [Part 5: Generative adversarial networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part05_gans.ipynb)\n* [Chapter 13: Best practices for the real world](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_best-practices-for-the-real-world.ipynb)\n* [Chapter 14: Conclusions](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_conclusions.ipynb)\n","funding_links":[],"categories":["Jupyter Notebook","A01_机器学习教程","📚 Skill Development \u0026 Career","Interesting Non EO parts Python","Table of Contents"],"sub_categories":["Curated Jupyter Notebooks","GEDI","ML\u0026DL"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffchollet%2Fdeep-learning-with-python-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffchollet%2Fdeep-learning-with-python-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffchollet%2Fdeep-learning-with-python-notebooks/lists"}