[PDF.68og] Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)

Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)

[PDF.fv36] Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)

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 | #1772614 in Books |  2009-10-28 | Original language:English | PDF # 1 |  9.21 x.31 x6.14l,.46 | File type: PDF | 144 pages

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||7 of 7 people found the following review helpful.| Excellent theoretical and mathematical treatment of DL.|By Allan Campbell|Thorough mathematical treatment of deep learning. Extensive body of references to the academic literature. Offers a list of open problems. An excellent concise academic reference.|0 of 2 people found the following review helpful.| Nope|By Marc J. Vandenplas

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 ...

You easily download any file type for your device.Learning Deep Architectures for AI (Foundations and Trends(r) in Machine Learning)   |  Yoshua Bengio. A good, fresh read, highly recommended.

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