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list price: $80.00 USD
edition:Hardcover
category: Computers
published: Nov 2016
ISBN:9780262035613
publisher: The MIT Press

Deep Learning

by Ian Goodfellow; Aaron Courville & Yoshua Bengio

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intelligence (ai) & semantics, computer science
0 of 5
0 ratings
rated!
rated!
list price: $80.00 USD
edition:Hardcover
category: Computers
published: Nov 2016
ISBN:9780262035613
publisher: The MIT Press
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.

About the Authors
Ian Goodfellow is a Research Scientist at Google.
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Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.
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Yoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Editorial 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

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