内容简介
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.
【展开】
【收起】
下载说明
1、追日是作者栎年创作的原创作品,下载链接均为网友上传的的网盘链接!
2、相识电子书提供优质免费的txt、pdf等下载链接,所有电子书均为完整版!
下载链接