• Title Learning Deep Architectures for AI
• Author(s) Yoshua Bengio
• Publisher: Now Publishers Inc (October 28, 2009)
• Hardcover/Paperback 144 pages
• eBook PDF (131 pages)
• Language: English
• ISBN-10: 1601982941
• ISBN-13: 978-1601982940
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Book Description

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.

• Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Data Science
• Artificial Intelligence
• Data Analysis and Data Mining

• Learning Deep Architectures for AI (Yoshua Bengio)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• The Mirror Site (3) - PDF

• Deep Learning (Ian Goodfellow, et al)
• Deep Learning Tutorials (LISA Lab)
• Neural Networks and Deep Learning (Michael Nielsen)
• Understanding Machine Learning: From Theory to Algorithms