Learning tensorflow : A guide to building deep learning systems

By:

Hope, Tom

Contributor(s):

Language: ENG Publication details: Sebastopol O'Reilly Media, Inc. 2017Edition: 1stDescription: x, 228p. | Binding - Paperback | 23*18 cmISBN:

9789352136100

Subject(s):

Computer Engineering

DDC classification:

DDC23 006.31 HOP/RES

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 audiencefrom data scientists and engineers to students and researchers. Youll 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, youll know how to build and deploy production-ready deep learning systems in TensorFlow.

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Item type	Current library	Collection	Call number	Status	Barcode
Books	School of Engineering & Technology Reference Section	Reference	006.31 HOP/RES (Browse shelf(Opens below))	Not For Loan	E14581
Books	School of Engineering & Technology General Stacks	Circulation	006.31 HOP/RES (Browse shelf(Opens below))	Available	E14582

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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 audiencefrom data scientists and engineers to students and researchers. Youll 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, youll know how to build and deploy production-ready deep learning systems in TensorFlow.

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