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Learning Tensor Flow: A Guide to Building Deep Learning Systems Paperback – 2017

4.6 out of 5 stars 3 customer reviews

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  • Learning Tensor Flow: A Guide to Building Deep Learning Systems
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Product description

About the Author

Tom Hope

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

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

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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Product details

  • Reading level: 10+ years
  • Paperback: 244 pages
  • Publisher: Shroff/O'Reilly; First edition (2017)
  • Language: English
  • ISBN-10: 9789352136100
  • ISBN-13: 978-9352136100
  • ASIN: 9352136101
  • Product Dimensions: 22.9 x 17.8 x 1.1 cm
  • Average Customer Review: 4.6 out of 5 stars 3 customer reviews
  • Amazon Bestsellers Rank: #60,781 in Books (See Top 100 in Books)
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3 customer reviews

4.6 out of 5 stars

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9 February 2018
Format: PaperbackVerified Purchase
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Most helpful customer reviews on Amazon.com

Amazon.com: 3.6 out of 5 stars 24 reviews
Abel Brown
1.0 out of 5 starsTotally Uninspired -- Just Read the Online Docs for Free
21 September 2017 - Published on Amazon.com
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51 people found this helpful.
Manhattan Lenape
1.0 out of 5 starshodgepodge of collected code without proper explanations
23 October 2017 - Published on Amazon.com
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18 people found this helpful.
H. Rast
1.0 out of 5 starssave your money and read the docs
10 March 2018 - Published on Amazon.com
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9 people found this helpful.
Eric D. Brown
1.0 out of 5 starsNot a good book. You are better off reading the docs and online tutorials
24 June 2018 - Published on Amazon.com
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3 people found this helpful.
5.0 out of 5 starsReally nice, good price
6 December 2018 - Published on Amazon.com
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