Deep_Learning_Architecture_for_AI.pdf

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Theoretical results suggest that in order to learn the kind of com- plicated functions that can represent high-level abstractions (e.g., in vision, language, and other AI-level tasks), one may need deep architec- tures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in com- plicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state- of-the-art in certain areas. This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

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