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Learning TensorFlow: A Guide to Building Deep Learning Systems Pages : 242 - Edition : 0 - Type : pdf

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Learning TensorFlow: A Guide to Building Deep Learning Systems (Tom Hope, Yehezkel Resheff, Itay Lieder)

Shared by le kaka on 2017-10-28

Learning TensorFlow: A Guide to Building Deep Learning Systems Author: Tom Hope, Yehezkel Resheff, Itay Lieder Pub Date: 2017 ISBN: 978-1491978511 Pages: 242 Language: English Format: PDF/EPUB/AZW3 Size: 17 Mb 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. 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

tags  Tags:  TensorFlow ,Deep Learning 

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