Learning TensorFlow
A Guide to Building Deep Learning Systems
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Authors  Itay Lieder, Yehezkel Resheff, Tom Hope 
Publisher  O'Reilly Media 
Published  2017 
Pages  242 
Language  English 
Format  Paper book / ebook 
ISBN10  1491978511 
ISBN13  9781491978511 
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an endtoend guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a handson approach to TensorFlow fundamentals for a broad technical audience  from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy productionready deep learning systems in TensorFlow.
Get up and running with TensorFlow, rapidly and painlessly; Learn how to use TensorFlow to build deep learning models from the ground up; Train popular deep learning models for computer vision and NLP; Use extensive abstraction libraries to make development easier and faster; Learn how to scale TensorFlow, and use clusters to distribute model training; Deploy TensorFlow in a production setting.
 Itay Lieder
 Yehezkel Resheff
 Tom Hope
4 5 33
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