Deep Learning by Yoshua Bengio - ISBN: 9780262035613
Hardcover
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.
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RRP$249.99

$191.55

  • Hardcover

    800 pages

  • Release Date

    18 November 2016

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Summary

Plt;bPgt;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.Plt;/bPgt;Plt;pPgt;P#8220;Written by three experts in the field, Plt;iPgt;Deep LearningPlt;/iPgt; is the only comprehensive book on the subject.P#8221;Plt;brPgt;Plt;bPgt;P#8212;Elon MuskPlt;/bPgt;, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXPlt;/pPgt;Plt;pPgt;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. Plt;/pPgt;Plt;pPgt;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. Plt;/pPgt;Plt;pPgt;Plt;iPgt;Deep LearningPlt;/iPgt; 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.Plt;/pPgt;

Book Details

ISBN-13:9780262035613
ISBN-10:0262035618
Author:Yoshua Bengio, Ian Goodfellow, Aaron Courville
Publisher:MIT Press Ltd
Imprint:MIT Press
Format:Hardcover
Number of Pages:800
Release Date:18 November 2016
Weight:1.30kg
Dimensions:25mm x 178mm x 229mm
Series:Adaptive Computation and Machine Learning series
Audience Age:18
A-Format
B-Format
Deep Learning by Yoshua Bengio - ISBN: 9780262035613
178 × 229 mm
C-Format
A4
mm / in
What They're Saying

Critics Review

[T]he 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.

– Daniel D. Gutierrez * insideBIGDATA *

About The Author

Yoshua Bengio

Ian Goodfellow is a Research Scientist at Google.Plt;brPgt;Plt;brPgt;Yoshua Bengio is Professor of Computer Science at the UniversitP#233; de MontrP#233;al.Plt;brPgt;Plt;brPgt;Aaron Courville is Assistant Professor of Computer Science at the UniversitP#233; de MontrP#233;al.

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