Deep Learning with Python
Author: Francois Chollet
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Brief contents
PART 1 – FUNDAMENTALS OF DEEP LEARNING ……………………………. 1
1 What is deep learning? ……………….. 3
2 Before we begin: the mathematical building blocks of neural networks ……………….. 25
3 Getting started with neural networks ……………….. 56
4 Fundamentals of machine learning ……………….. 93
PART 2 – DEEP LEARNING IN PRACTICE …………………………………. 117
5 Deep learning for computer vision ……………….. 119
6 Deep learning for text and sequences ……………….. 178
7 Advanced deep-learning best practices ……………….. 233
8 Generative deep learning ……………….. 269
9 Conclusions ……………….. 314
preface
If you’ve picked up this book, you’re probably aware of the extraordinary progress that deep learning has represented for the field of artificial intelligence in the recent past. In a mere five years, we’ve gone from near-unusable image recognition and speech transcription, to superhuman performance on these tasks.
The consequences of this sudden progress extend to almost every industry. But in order to begin deploying deep-learning technology to every problem that it could solve, we need to make it accessible to as many people as possible, including nonexperts—people who aren’t researchers or graduate students. For deep learning to reach its full potential, we need to radically democratize it.
When I released the first version of the Keras deep-learning framework in March 2015, the democratization of AI wasn’t what I had in mind. I had been doing research in machine learning for several years, and had built Keras to help me with my own experiments. But throughout 2015 and 2016, tens of thousands of new people entered the field of deep learning; many of them picked up Keras because it was—and still is—the easiest framework to get started with. As I watched scores of newcomers use Keras in unexpected, powerful ways, I came to care deeply about the accessibility and democratization of AI. I realized that the further we spread these technologies, the more useful and valuable they become. Accessibility quickly became an explicit goal in the development of Keras, and over a few short years, the Keras developer community has made fantastic achievements on this front. We’ve put deep learning into the hands of tens of thousands of people, who in turn are using it to solve important problems we didn’t even know existed until recently.
The book you’re holding is another step on the way to making deep learning available to as many people as possible. Keras had always needed a companion course to Licensed to xiv PREFACE simultaneously cover fundamentals of deep learning, Keras usage patterns, and deeplearning best practices. This book is my best effort to produce such a course. I wrote it with a focus on making the concepts behind deep learning, and their implementation, as approachable as possible. Doing so didn’t require me to dumb down anything—I strongly believe that there are no difficult ideas in deep learning. I hope you’ll find this book valuable and that it will enable you to begin building intelligent applications and solve the problems that matter to you.