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文件名称：Deep_Learning_Architecture_for_AI.pdf
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更新时间：2018-01-04 04:12:31
机器学习 learning 深度学习
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.