Learning Deep Architectures for AI (Yoshua Bengio)

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Learning Deep Architectures for AI (Yoshua Bengio)

Can AI be produced via machine learning? Deep architectures are necessary, according to theoretical findings, inspiration from the brain and cognition, and machine learning studies, in order to learn the kind of complex functions that can represent high-level abstractions (such as in a vision, language, and other AI-level tasks).

Deep architectures are made up of multiple levels of non-linear processes, such as the many hidden layers in neural nets, the many levels of latent variables in graphical models, or the numerous sub-formulas used in complex propositional formulas. Each level of architecture reflects features that are composed of lower-level characteristics and are at a different level of abstraction. Searching the parameter space of deep architectures is a challenging undertaking, however, since these discoveries in 2006, additional algorithms have been found and a new sub-area has formed in the machine learning community.

Deep architectures have recently been proposed to be trained using learning algorithms like those for Deep Belief Networks and other similar unsupervised learning algorithms, producing intriguing results and surpassing the state-of-the-art in several domains.

The book Learning Deep Architectures for AI explores the rationale behind and guiding principles of deep architecture learning algorithms. Recent results using various learning algorithms for deep architectures are analyzed and compared, and arguments for their success are put forth and discussed. This highlights problems and suggests directions for further research in this area.

Ebook Details

About the Authors
Yoshua Bengio is a Canadian computer scientist who is most known for his work on deep learning and artificial neural networks. He is a professor at the Université de Montréal's Department of Computer Science and Operations Research as well as the Montreal Institute for Learning Algorithms' scientific director (MILA).
Published Date / Year
(October 28, 2009)
144 pages
eBook Format
PDF (131 pages)

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