https://github.com/fchollet/deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"
https://github.com/fchollet/deep-learning-with-python-notebooks

Last synced: 5 days ago
JSON representation

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

• Host: GitHub
• URL: https://github.com/fchollet/deep-learning-with-python-notebooks
• Owner: fchollet
• License: mit
• Created: 2017-09-05T19:47:56.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-07-09T22:05:49.000Z (7 months ago)
• Last Synced: 2025-01-13T20:07:50.208Z (12 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 6.67 MB
• Stars: 18,902
• Watchers: 653
• Forks: 8,718
• Open Issues: 178
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• StarryDivineSky - fchollet/deep-learning-with-python-notebooks
• ultimate-awesome - Deep Learning with Python

README

        
# Companion Jupyter notebooks for the book "Deep Learning with Python"

This 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&a_bid=76564dff).

For readability, these notebooks only contain runnable code blocks and section titles, and omit everything else in the book: text paragraphs, figures, and pseudocode.

**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.**

These notebooks use TensorFlow 2.6.

## Table of contents

* [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)

* [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)

* [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)

* [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)

* [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)

* [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)

* Chapter 9: Advanced deep learning for computer vision

    - [Part 1: Image segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part01_image-segmentation.ipynb)

    - [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)

    - [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)

* [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)

* Chapter 11: Deep learning for text

    - [Part 1: Introduction](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part01_introduction.ipynb)

    - [Part 2: Sequence models](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part02_sequence-models.ipynb)

    - [Part 3: Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part03_transformer.ipynb)

    - [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)

* Chapter 12: Generative deep learning

    - [Part 1: Text generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part01_text-generation.ipynb)

    - [Part 2: Deep Dream](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part02_deep-dream.ipynb)

    - [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)

    - [Part 4: Variational autoencoders](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part04_variational-autoencoders.ipynb)

    - [Part 5: Generative adversarial networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part05_gans.ipynb)

* [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)

* [Chapter 14: Conclusions](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_conclusions.ipynb)