Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning) by Yoshua Bengio
English | Oct. 28, 2009 | ISBN: 1601982941 | 130 Pages | PDF | 1 MB
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.