TensorFlow for Deep Learning

Preface

This book will introduce you to the fundamentals of machine learning through TensorFlow. TensorFlow is Google’s new software library for deep learning that makes it straightforward for engineers to design and deploy sophisticated deep learning architectures. You will learn how to use TensorFlow to build systems capable of detecting objects in images, understanding human text, and predicting the properties of potential medicines. Furthermore, you will gain an intuitive understanding of TensorFlow’s potential as a system for performing tensor calculus and will learn how to use TensorFlow for tasks outside the traditional purview of machine learning.

Importantly, TensorFlow for Deep Learning is one of the first deep learning books written for practitioners. It teaches fundamental concepts through practical examples and builds understanding of machine learning foundations from the ground up. The target audience for this book is practicing developers, who are comfortable with designing software systems, but not necessarily with creating learning systems. At times we use some basic linear algebra and calculus, but we will review all necessary fundamentals. We also anticipate that our book will prove useful for scientists and other professionals who are comfortable with scripting, but not necessarily with designing learning algorithms.

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Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/matroid/dlwithtf.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “TensorFlow for Deep Learning by Bharath Ramsundar and Reza Bosagh Zadeh (O’Reilly). Copyright 2018 Reza Zadeh, Bharath Ramsundar, 978-1-491-98045-3.”

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [e-mail].

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Acknowledgments

Bharath is thankful to his PhD advisor for letting him work on this book during his nights and weekends, and especially thankful to his family for their unstinting support during the entire process.

Reza is thankful to the open source communities on which much of software and computer science is based. Open source software is one of the largest concentrations of human knowledge ever created, and this book would have been impossible without the entire community behind it.