Deep Learning with Python

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.

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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, TensorFlow, or machine learning is required.

About the Author

François Chollet is one of the most important researchers in modern day deep learning. His groundbreaking work includes the creation of the Keras deep learning library, and major contributions to the TensorFlow framework. These tools have helped revolutionize and democratize deep learning. François is an AI researcher and Senior Staff Software Engineer at Google. François authored Deep Learning with R alongside J.J. Allaire, and developed the Abstraction and Reasoning Challenge that measures AI skill-acquisition on unknown tasks.

Table of Contents

1. What is deep learning?
2. Before we begin: the mathematical building blocks of neural networks
3. Getting started with neural networks
4. Fundamentals of machine learning
5. Deep learning for computer vision
6. Deep learning for text and sequences
7. Advanced deep-learning best practices
8. Generative deep learning
9. Conclusions
10. appendix A - Installing Keras and its dependencies on Ubuntu
11. appendix B - Running Jupyter notebooks on an EC2 GPU instance

PART 1 - FUNDAMENTALS OF DEEP LEARNING
PART 2 - DEEP LEARNING IN PRACTICE
From the Publisher

For over thirty years, Manning Publications has been delivering impeccable quality in tech publishing. Our rich and independent history is filled with innovations, including groundbreaking early access programs, DRM-free ebooks, and live learning projects. We spend thousands of hours making each Manning book outstanding--and our readers agree! We're regularly told that Manning produces the very best tech content you can buy.

Manning authors are technology experts, including distinguished academics, industry veterans, and the creators of major tools. Timeless Manning classics include Francois Chollet's Deep Learning with Python, Jon Skeet's C# in Depth, Don Jones' Learn Windows Powershell in a Month of Lunches, and Chris Richarson's Microservices Patterns. We're proud to help some of the world's greatest programmers share their unique insight with you.

Author: Francois Chollet
Publisher: Manning Publications
Published: 12/22/2017
Pages: 384
Binding Type: Paperback
Weight: 1.50lbs
Size: 9.25h x 7.38w x 0.81d
ISBN: 9781617294433

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 TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and 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.