Summary

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning-a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

What's Inside

Deep learning from first principles

Setting up your own deep-learning environment

Image-classification models

Deep learning for text and sequences

Neural style transfer, text generation, and image generation

About the Reader

Readers need intermediate Python skills. No previous experience with Keras, Tensor Flow, or machine learning is required.

About the Author

François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the Tensor Flow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING

What is deep learning?

Before we begin: the mathematical building blocks of neural networks

Getting started with neural networks

Fundamentals of machine learning

PART 2 - DEEP LEARNING IN PRACTICE

Deep learning for computer vision

Deep learning for text and sequences

Advanced deep-learning best practices

Generative deep learning

Conclusions

appendix A - Installing Keras and its dependencies on Ubuntu

appendix B - Running Jupiter notebooks on an EC2 GPU instance.