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Jupyter notebooks for the code samples of the book "Deep Learning with Python"
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| 2026-07-31 | 20216 |
| 2026-08-01 | 20216 |
| 2026-08-06 | 20225 |
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# Companion notebooks for Deep Learning with Python 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) 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) and the [first edition (2017)](https://www.manning.com/books/deep-learning-with-python?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.** ## Running the code We recommend running these notebooks on [Colab](https://colab.google), which provides a hosted runtime with all the dependencies you will need. You can also, run these notebooks locally, either by setting up your own Jupyter environment, or using Colab's instructions for [running locally](https://research.google.com/colaboratory/local-runtimes.html). By default, all notebooks will run on Colab's free tier GPU runtime, which is sufficient to run all code in this book. Chapter 8-18 chapters will benefit from a faster GPU if you have a Colab Pro subscription. You can change your runtime type using **Runtime -> Change runtime type** in Colab's dropdown menus. ## Choosing a backend The code for third edition is written using Keras 3. As such, it can be run with JAX, TensorFlow or PyTorch as a backend. To set the backend, update the backend in the cell at the top of the colab that looks like this: ```python import os os.environ["KERAS_BACKEND"] = "jax" ``` This must be done only once per session before importing Keras. If you are in the middle running a notebook, you will need to restart the notebook session and rerun all relevant notebook cells. This can be done in using **Runtime -> Restart Session** in Colab's dropdown menus. ## Using Kaggle data This book uses datasets and model weights provided by Kaggle, an online Machine Learning community and platform. You will need to create a Kaggle login to run Kaggle code in this book; instructions are given in Chapter 8. For chapters that need Kaggle data, you can login to Kaggle once per session when you hit the notebook cell with `kagglehub.login()`. Alternately, you can set up your Kaggle login information once as Colab secrets: * Go to https://www.kaggle.com/ and sign in. * Go to https://www.kaggle.com/settings and generate a Kaggle API key. * Open the secrets tab in Colab by clicking the key icon on the left. * Add two secrets, `KAGGLE_USERNAME` and `KAGGLE_KEY` with the username and key you just created. Following this approach you will only need to copy your Kaggle secret key once, though you will need to allow each notebook to access your secrets when running the relevant Kaggle code. ## 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 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) * [Chapter 4: Classification and regression](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_classification-and-regression.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: A deep dive on Keras](https:/
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François Chollet
36
Matt Watson · Google · United States
16
5
Derek Chia · Singapore
2
2
Aziz Alto
1
1
Rama Ramakrishnan
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:503dddbfff6822aa, llm:Description: 'Jupyter notebooks for the code samples of the book "Deep Learning with Python"' (fchollet repository). Language: Jupyter Notebook.
matched fp:503dddbfff6822aa, llm:Description: 'Jupyter notebooks for the code samples of the book "Deep Learning with Python"' (fchollet repository). Language: Jupyter Notebook.