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Name		Name	Last commit message	Last commit date
first_edition		first_edition
LICENSE		LICENSE
README.md		README.md
chapter02_mathematical-building-blocks.ipynb		chapter02_mathematical-building-blocks.ipynb
chapter03_introduction-to-keras-and-tf.ipynb		chapter03_introduction-to-keras-and-tf.ipynb
chapter04_getting-started-with-neural-networks.ipynb		chapter04_getting-started-with-neural-networks.ipynb
chapter05_fundamentals-of-ml.ipynb		chapter05_fundamentals-of-ml.ipynb
chapter07_working-with-keras.ipynb		chapter07_working-with-keras.ipynb
chapter08_intro-to-dl-for-computer-vision.ipynb		chapter08_intro-to-dl-for-computer-vision.ipynb
chapter09_part01_image-segmentation.ipynb		chapter09_part01_image-segmentation.ipynb
chapter09_part02_modern-convnet-architecture-patterns.ipynb		chapter09_part02_modern-convnet-architecture-patterns.ipynb
chapter09_part03_interpreting-what-convnets-learn.ipynb		chapter09_part03_interpreting-what-convnets-learn.ipynb
chapter10_dl-for-timeseries.ipynb		chapter10_dl-for-timeseries.ipynb
chapter11_part01_introduction.ipynb		chapter11_part01_introduction.ipynb
chapter11_part02_sequence-models.ipynb		chapter11_part02_sequence-models.ipynb
chapter11_part03_transformer.ipynb		chapter11_part03_transformer.ipynb
chapter11_part04_sequence-to-sequence-learning.ipynb		chapter11_part04_sequence-to-sequence-learning.ipynb
chapter12_part01_text-generation.ipynb		chapter12_part01_text-generation.ipynb
chapter12_part02_deep-dream.ipynb		chapter12_part02_deep-dream.ipynb
chapter12_part03_neural-style-transfer.ipynb		chapter12_part03_neural-style-transfer.ipynb
chapter12_part04_variational-autoencoders.ipynb		chapter12_part04_variational-autoencoders.ipynb
chapter12_part05_gans.ipynb		chapter12_part05_gans.ipynb
chapter13_best-practices-for-the-real-world.ipynb		chapter13_best-practices-for-the-real-world.ipynb
chapter14_conclusions.ipynb		chapter14_conclusions.ipynb

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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).

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
Chapter 3: Introduction to Keras and TensorFlow
Chapter 4: Getting started with neural networks: classification and regression
Chapter 5: Fundamentals of machine learning
Chapter 7: Working with Keras: a deep dive
Chapter 8: Introduction to deep learning for computer vision
Chapter 9: Advanced deep learning for computer vision
Chapter 10: Deep learning for timeseries
Chapter 11: Deep learning for text
Chapter 12: Generative deep learning
Chapter 13: Best practices for the real world
Chapter 14: Conclusions

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

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