Название: Learning Deep Architectures for AI Автор: Yoshua Bengio Издательство: Now Год: 2010 Формат: pdf Страниц: 130 Размер: 1 mb. Язык: English
Theoretical results 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 may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated 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 stateof-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.
Advances in Deep Learning Название: Advances in Deep Learning Автор: M. Arif Wani, Farooq Ahmad Bhat Издательство: Springer ISBN: 9811367930 Год: 2019 (2020 Edition)...
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Learning Deep Architectures for AI 
