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Viser: Learning TensorFlow - A Guide to Building Deep Learning Systems
Learning TensorFlow Vital Source e-bog
Tom Hope, Yehezkel S. Resheff og Itay Lieder
(2017)
Learning TensorFlow
A Guide to Building Deep Learning Systems
Tom Hope, Yehezkel S. Resheff og Itay Lieder
(2017)
Sprog: Engelsk
om ca. 10 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: O'Reilly Media, Inc (August 2017)
- Forfattere: Tom Hope, Yehezkel S. Resheff og Itay Lieder
- ISBN: 9781491978467
Bookshelf online: 5 år fra købsdato.
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Print: 2 sider kan printes ad gangen
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Detaljer om varen
- 1. Udgave
- Paperback: 242 sider
- Udgiver: O'Reilly Media, Incorporated (September 2017)
- Forfattere: Tom Hope, Yehezkel S. Resheff og Itay Lieder
- ISBN: 9781491978511
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
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