Deep Learning

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Ian Goodfellow (Senior Research Scientist, OpenAI) Yoshua Bengio (Full Professor, Université de Montreal)

Deep Learning

Ian Goodfellow (Senior Research Scientist, OpenAI), Yoshua Bengio (Full Professor, Université de Montreal), Aaron Courville (Assistant Professor, Université de Montréal )

$240

Hardback

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English

Massachusetts Inst of Tec
18 November 2016

Mathematics & Sciences; Machine learning; Neural networks & fuzzy systems

Series: Deep Learning

"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."

By: Ian Goodfellow (Senior Research Scientist OpenAI), Yoshua Bengio (Full Professor, Université de Montreal), Aaron Courville (Assistant Professor, Université de Montréal )
Imprint: Massachusetts Inst of Tec
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm, Spine: 25mm
Weight: 1.270kg
ISBN: 9780262035613
ISBN 10: 0262035618
Series: Deep Learning
Pages: 800
Publication Date: 18 November 2016
Recommended Age: From 18 years
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Ian Goodfellow is Research Scientist at OpenAI. Yoshua Bengio is Professor of Computer Science at the Universite de Montreal. Aaron Courville is Assistant Professor of Computer Science at the Universite de Montreal.

Reviews for Deep Learning

[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