Francois Chollet [Chollet - Deep Learning With Python

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If youve picked up this book, youre 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, weve 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 non-expertspeople who arent 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 wasnt 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 wasand still isthe 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. Weve put deep learning into the hands of tens of thousands of people, who in turn are using it to solve important problems we didnt even know existed until recently.

The book youre 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 simultaneously cover fundamentals of deep learning, Keras usage patterns, and deep-learning 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 didnt require me to dumb down anythingI strongly believe that there are no difficult ideas in deep learning. I hope youll find this book valuable and that it will enable you to begin building intelligent applications and solve the problems that matter to you.

Id like to thank the Keras community for making this book possible. Keras has grown to have hundreds of open source contributors and more than 200,000 users. Your contributions and feedback have turned Keras into what it is today.

Id also like to thank Google for backing the Keras project. It has been fantastic to see Keras adopted as TensorFlows high-level API. A smooth integration between Keras and TensorFlow greatly benefits both TensorFlow users and Keras users and makes deep learning accessible to most.

I want to thank the people at Manning who made this book possible: publisher Marjan Bace and everyone on the editorial and production teams, including Christina Taylor, Janet Vail, Tiffany Taylor, Katie Tennant, Dottie Marsico, and many others who worked behind the scenes.

Many thanks go to the technical peer reviewers led by Aleksandar DragosavljeviDiego Acua Rozas, Geoff Barto, David Blumenthal-Barby, Abel Brown, Clark Dorman, Clark Gaylord, Thomas Heiman, Wilson Mar, Sumit Pal, Vladimir Pasman, Gustavo Patino, Peter Rabinovitch, Alvin Raj, Claudio Rodriguez, Srdjan Santic, Richard Tobias, Martin Verzilli, William E. Wheeler, and Daniel Williamsand the forum contributors. Their contributions included catching technical mistakes, errors in terminology, and typos, and making topic suggestions. Each pass through the review process and each piece of feedback implemented through the forum topics shaped and molded the manuscript.

On the technical side, special thanks go to Jerry Gaines, who served as the books technical editor; and Alex Ott and Richard Tobias, who served as the books technical proofreaders. Theyre the best technical editors I could have hoped for.

Finally, Id like to express my gratitude to my wife Maria for being extremely supportive throughout the development of Keras and the writing of this book.

This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether youre a practicing machine-learning engineer, a software developer, or a college student, youll find value in these pages.

This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning.

Youll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems.

The code examples use the Python deep-learning framework Keras, with TensorFlow as a backend engine. Keras, one of the most popular and fastest-growing deep-learning frameworks, is widely recommended as the best tool to get started with deep learning.

After reading this book, youll have a solid understand of what deep learning is, when its applicable, and what its limitations are. Youll be familiar with the standard workflow for approaching and solving machine-learning problems, and youll know how to address commonly encountered issues. Youll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.