- 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: 3 customer reviews
- Amazon Bestsellers Rank: #60,781 in Books (See Top 100 in Books)
Learning Tensor Flow: A Guide to Building Deep Learning Systems Paperback – 2017
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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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Although the book can be Improved by First explaining all the operations (i.e - chapter 3 ) before scaring new people with Example 2 :D, as it was explained below Example 2 IT SIMPLIFIES...
So 5 Stars...
Most helpful customer reviews on Amazon.com
I am just amazed that these three authors were so incredibly lazy that having giving the opportunity to write a book, they decided not to spend even couple of weeks on it. if each of these three authors spend a month on the book, the book could have been something useful. They just decided to take the money and run.
The editors Nicole Tache and Shiny Kalapurakkel should be ashamed of themselves for letting a hodgepodge of collected code to be published as a book. And shame on O'Reilly for publishing this.
I suspect the positive ratings are by the authors's friends.
Dont buy this book. Go online and find the countless tensorflow tutorials that are available for free.