Learning TensorFlow A Guide to Building Deep Learning Systems 2017.8

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Learning TensorFlow: A Guide to Building Deep Learning SystemsBy 作者: Tom Hope – Yehezkel S. Resheff – Itay LiederISBN-10 书号: 1491978511ISBN-13 书号: 9781491978511 Edition 版本: 1Release 出版日期: 2017-08-28pages 页数: (242)List Price: $49.99Book DescriptionRoughly 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 painlesslyLearn how to use TensorFlow to build deep learning models from the ground upTrain popular deep learning models for computer vision and NLPUse extensive abstraction libraries to make development easier and fasterLearn how to scale TensorFlow, and use clusters to distribute model trainingDeploy TensorFlow in a production settingContentsChapter 1 IntroductionChapter 2 Go with the Flow: Up and running with TensorFlowChapter 3 Understanding TensorFlow BasicsChapter 4 Convolutional Neural NetworksChapter 5 Working with Text and Sequences + TensorBoard visualizationChapter 6 TF Abstractions and SimplificationChapter 7 Queues, Threads, and Reading DataChapter 8 Distributed TensorFlowChapter 9 Serving ModelsChapter 10 Miscellaneous  
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