Update README.md

Author: fchollet

README.md

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-# Companion Jupyter notebooks for the book "Deep Learning with Python"
+# Companion Jupyter notebooks for the book "Deep Learning with Python" (2025)
 
-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).
+This repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python, third edition](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.**
 
-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 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: 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)
+* [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)