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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://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_deep-dive-keras.ipynb)
+* [Chapter 8: Image Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_image-classification.ipynb)
+* [Chapter 9: Convnet architecture patterns](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_convnet-architecture-patterns.ipynb)
+* [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)
+* [Chapter 11: Image Segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_image-segmentation.ipynb)
+* [Chapter 12: Object Detection](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_object-detection.ipynb)
+* [Chapter 13: Timeseries Forecasting](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_timeseries-forecasting.ipynb)
+* [Chapter 14: Text Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_text-classification.ipynb)
+* [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)
+* [Chapter 16: Text Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter16_text-generation.ipynb)
+* [Chapter 17: Image Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter17_image-generation.ipynb)
+* [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)