Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder


Download PDF Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

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Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

Download PDF Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

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Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder

TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers.

With this book you will learn how to:

  • Get up and running with TensorFlow, rapidly and painlessly
  • Build and train popular deep learning models for computer vision and NLP
  • Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs
  • Train models at scale, and deploy TensorFlow in a production setting
  • Sales Rank: #54047 in Books
  • Published on: 2017-06-25
  • Original language: English
  • Dimensions: 9.84″ h x .59″ w x 5.91″ l,
  • Binding: Paperback
  • 250 pages

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.

Hezi Reshef is an applied researcher and PhD student in Machine Learning at the Hebrew University, developing Machine Learning and Deep Learning methods for wearable device data, and working on using wearable devices to monitor patient health. He has worked at Intel Corp., leading Deep Learning R&D for monitoring and predicting patient outcomes using remote sensing and wearables. Prior to Intel, Hezi was at Microsoft, leading Machine Learning R&D for mining telemetry data, predicting software bugs, user segmentation, and other projects.

Itay Lieder is an applied researcher in Machine Learning and Computational Neuroscience and a PhD student at the Hebrew University, in collaboration with the Gatsby Computational Neuroscience Unit at UCL, studying the human perception with massive crowd-sourcing experiments on Amazon Turk. His current work focuses on predicting and understanding the way humans react to sounds (e.g. music), via multiple online interactive experiments. He has worked for large international corporations, leading Deep Learning R&D in text analytics and web mining for sales and marketing.

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Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder PDF
Learning TensorFlow: A guide to building deep learning systems, by Tom Hope, Yehezkel S. Resheff, Itay Lieder PDF

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