Learning TensorFlow
A Guide to Building Deep Learning Systems
Tom Hope, Yehezkel S. Resheff, and Itay Lieder
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Learning TensorFlow
by Tom Hope, Yehezkel S. Resheff, and Itay Lieder Copyright © 2017 Tom Hope, Itay
Lieder, and Yehezkel S. Resheff. All rights reserved.
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August 2017: First Edition
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Revision History for the First Edition
2017-08-04: First Release
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978-1-491-97851-1
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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:
1. Get up and running with TensorFlow, rapidly and painlessly.
2. Use TensorFlow to build models from the ground up.
3. Train and understand popular deep learning models for computer vision and
NLP.
4. Use extensive abstraction libraries to make development easier and faster.
5. Scale up TensorFlow with queuing and multithreading, training on clusters,
and serving output in production.
6. 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.
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