Preface
Deep learning has emerged in the last few years as a premier technology for building intelligent systems that learn from data. Deep neural networks, originally roughly inspired by how the human brain learns, are trained with large amounts of data to solve complex tasks with unprecedented accuracy. With open source frameworks making this technology widely available, it is becoming a must-know for anybody involved with big data and machine learning.
TensorFlow is currently the leading open source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students, and researchers. The book adopts a hands-on approach suitable for a broad technical audience, allowing beginners a gentle start while diving deep into advanced topics and showing how to build production-ready systems.
In this book you will learn how to:
- Get up and running with TensorFlow, rapidly and painlessly.
- Use TensorFlow to build models from the ground up.
- Train and understand popular deep learning models for computer vision and NLP.
- Use extensive abstraction libraries to make development easier and faster.
- Scale up TensorFlow with queuing and multithreading, training on clusters, and serving output in production.
- And much more!
This book is written by data scientists with extensive R&D experience in both industry and academic research. The authors take a hands-on approach, combining practical and intuitive examples, illustrations, and insights suitable for practitioners seeking to build production-ready systems, as well as readers looking to learn to understand and build flexible and powerful models.
Prerequisites
This book assumes some basic Python programming know-how, including basic familiarity with the scientific library NumPy.
Machine learning concepts are touched upon and intuitively explained throughout the book. For readers who want to gain a deeper understanding, a reasonable level of knowledge in machine learning, linear algebra, calculus, probability, and statistics is recommended.
Conventions Used in This Book
The following typographical conventions are used in this book:
- Italic
-
Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width-
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
Constant width bold-
Shows commands or other text that should be typed literally by the user.
Constant width italic-
Shows text that should be replaced with user-supplied values or by values determined by context.
Using Code Examples
Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/Hezi-Resheff/Oreilly-Learning-TensorFlow.
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: “Learning TensorFlow by Tom Hope, Yehezkel S. Resheff, and Itay Lieder (O’Reilly). Copyright 2017 Tom Hope, Itay Lieder, and Yehezkel S. Resheff, 978-1-491-97851-1.”
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [email protected].
O’Reilly Safari
Note
Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals.
Members have access to thousands of books, training videos, Learning Paths, interactive tutorials, and curated playlists from over 250 publishers, including O’Reilly Media, Harvard Business Review, Prentice Hall Professional, Addison-Wesley Professional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others.
For more information, please visit http://oreilly.com/safari.
How to Contact Us
Please address comments and questions concerning this book to the publisher:
- O’Reilly Media, Inc.
- 1005 Gravenstein Highway North
- Sebastopol, CA 95472
- 800-998-9938 (in the United States or Canada)
- 707-829-0515 (international or local)
- 707-829-0104 (fax)
We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/learning-tensorflow.
To comment or ask technical questions about this book, send email to [email protected].
For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com.
Find us on Facebook: http://facebook.com/oreilly
Follow us on Twitter: http://twitter.com/oreillymedia
Watch us on YouTube: http://www.youtube.com/oreillymedia
Acknowledgments
The authors would like to thank the reviewers who offered feedback on this book: Chris Fregly, Marvin Bertin, Oren Sar Shalom, and Yoni Lavi. We would also like to thank Nicole Tache and the O’Reilly team for making it a pleasure to write the book.
Of course, thanks to all the people at Google without whom TensorFlow would not exist.