Learning Tensorflow : A Guide to Building Deep Learning Systems
Language: English
Published by O'reilly Media Sep 2017, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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US$ 74.40
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Neuware - 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.…
Seller Inventory # 9781491978511
- Title
- Learning Tensorflow : A Guide to Building Deep Learning Systems
- Author
- Tom Hope
- Publisher
- O'reilly Media Sep 2017
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1491978511
- ISBN 13
- 9781491978511
- Item weight
- 394 grams
- Dimensions
- 233x177x17 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
Tom Hope is an applied machine learning researcher and data scientist with extensive background in academia and industry.
He has background as a senior data scientist in large international corporation settings, leading data science and deep learning R&D across multiple domains including web mining, text analytics, computer vision,sales and marketing, IoT, financial forecasting and large-scale manufacturing. Previously he was at a successful e-commerce startup in its early days, leading data science R&D. He has also served as a data science consultant for major international companies and startups. His research in computer science, data mining and statistics revolves around machine learning, deep learning, NLP, weak supervision and time-series.
"About the title" may belong to another edition of this title.
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