Title:
Learning Deep Architectures for AI
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- AI-NN-PR
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- File Size:
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- Update:
- 2017-12-22
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- zeng
Description: 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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File list (Check if you may need any files):
| Filename | Size | Date |
|---|
| Learning Deep Architectures for AI.pdf | 1129870 | 2017-09-19 |
