Learning tensorflow : A guide to building deep learning systems
By: Hope, Tom
.
Contributor(s): Resheff, Yezezkel S | Lieder, Itay.
Publisher: Sebastopol O'Reilly Media, Inc. 2017Edition: 1st.Description: x, 228p. | Binding - Paperback | 23*18 cm.ISBN: 9789352136100.Subject(s): Computer EngineeringDDC classification: 006.31 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Text Books
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School of Engineering & Technology Reference Section | Reference | 006.31 HOP/RES (Browse shelf) | Not For Loan | E14581 | ||
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Text Books
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School of Engineering & Technology General Stacks | Circulation | 006.31 HOP/RES (Browse shelf) | Available | E14582 |
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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