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Learning Deep Architectures for AI (Foundations and Trends
Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.
144 pages, Paperback
First published October 28, 2009
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About the author
Yoshua Bengio
8Ìýbooks41ÌýfollowersYoshua Bengio is Professor of Computer Science at the Université de Montréal.
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Displaying 1 - 6 of 6 reviews
February 24, 2014
Basic concepts of deep learning are well explained in simple terms with more words than math; which I found useful and interesting.
June 27, 2018
Classical Latex-style monograph on complicated concepts that usually makes for a dry read. It is complementary to other books that rarely develop on the topic of deep belief networks, but I would have hoped for more illustrations of the basic concepts.
April 6, 2018
Slightly outdated already.
Shelved as 'to-keep-reference'
May 28, 2021Yoshua Bengio es uno de los padres del DL.
February 23, 2015
A bit outdated already, but still an invaluable reference work.
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April 22, 2015Location: GG6 IRC
Accession no: DL027014
Accession no: DL027014
Displaying 1 - 6 of 6 reviews
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