Learning Deep Architectures for AI

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In Machine Learning, Learning Deep Architectures for AI – This monograph discusses the engines and principles involved in learning algorithms for deep architectures, especially those that exploit the unsupervised learning of single-layer models such as blocks. build.

Summary:

Theoretical results suggest that to understand the type of complex function that can represent high-level abstraction (e.g. in vision, language, and other AI-level tasks), one might need deep architectures. Deep architectures include many levels of nonlinear operations, such as in neural networks with many hidden layers or in complex propositional formulas that reuse many subformulas. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as that of the Deep Belief Network have recently been proposed to solve this problem with remarkable success, pass the technical status in some areas. This monograph discusses the engines and principles related to learning algorithms for deep architectures, especially mining algorithms as the unsupervised learning building blocks of single-layer models such as limited Boltzmann machines, used to build deeper models, such as deep belief networks.

Learning Deep Architectures for AI

Can machine learning provide AI? Theoretical findings, brain inspiration and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that high-level abstractions can represent (e.g., in vision, language, and other of artificial intelligence), one would need deep architectures. Deep architectures consist of multiple layers of nonlinear operations, such as neural networks with many hidden layers, graphical models with many layers of latent variables, or complicated propositional formulas that reuse many sub-formulas. Each level of the architecture represents functions at a different level of abstraction, defined as a composition of lower level functions. Researching 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 in Deep Belief Networks and related unsupervised learning algorithms have recently been proposed to train deep architectures, producing interesting results and surpassing more advanced ones in certain areas. Deep Architecture Learning for AI discusses the rationale and principles of deep architecture learning algorithms. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations of their success are proposed and discussed, highlighting the challenges and suggesting avenues for future exploration in this area.

Learning Deep Architectures for AI

Author(s): Yoshua Bengio

Series: Foundations and Trends in Machine Learning

Publisher: Now Publishers Inc, Year: 2009

ISBN: 1601982941,9781601982940

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