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Deep Learning (Adaptive Computation and Machine Learning series) by [Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron]

Deep Learning (Adaptive Computation and Machine Learning series) Kindle Edition

5.0 out of 5 stars 1 customer review

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Length: 800 pages Language: English Format: Print Replica

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Review

The AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology.
 
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities. —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX  
 
“This is the definitive textbook on deep learning. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book. —Geoffrey Hinton FRS, Emeritus Professor, University of Toronto; Distinguished Research Scientist, Google

Product Description

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Product details

  • Format: Kindle Edition
  • File Size: 34654 KB
  • Print Length: 800 pages
  • Publisher: The MIT Press (10 November 2016)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B01MRVFGX4
  • Text-to-Speech: Not enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: 5.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #188,278 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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1 customer review

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26 November 2018
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Most helpful customer reviews on Amazon.com

Amazon.com: 3.8 out of 5 stars 217 reviews
slag
2.0 out of 5 starsA surprisingly poor book--who is the audience?
27 September 2017 - Published on Amazon.com
Verified Purchase
647 people found this helpful.
dolphone
2.0 out of 5 starsNot very good :(
10 November 2017 - Published on Amazon.com
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49 people found this helpful.
aflyax
2.0 out of 5 starsUnclear who the intended audience is — deep learning practitioners aren’t it, though...
8 July 2017 - Published on Amazon.com
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188 people found this helpful.
mackster
1.0 out of 5 starsA rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
16 May 2018 - Published on Amazon.com
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45 people found this helpful.
Amazon Customer
1.0 out of 5 starsIt does not worth the money for the quality of the print.
23 June 2018 - Published on Amazon.com
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16 people found this helpful.