Learning TensorFlow: a guide to building deep learning systems

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 end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks fo...

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Bibliographic Details
Main Authors: Hope, Tom (Author), Resheff, Yehezkel S. (Author), Lieder, Itay (Author)
Format: Electronic eBook
Language:English
Published: Sebastopol, CA O'Reilly Media 2017
Edition:First edition.
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Links:https://learning.oreilly.com/library/view/-/9781491978504/?ar
Summary: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 end-to-end 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 hands-on 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 production-ready deep learning systems in TensorFlow.
Item Description:Includes index. - Online resource; title from title page (Safari, viewed August 21, 2017)
Physical Description:1 Online-Ressource (1 volume) illustrations