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Learning Deep Architectures for AI (Paperback)
Loot Price: R2,142
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Learning Deep Architectures for AI (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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Can machine learning deliver AI? Theoretical results, inspiration
from the brain and cognition, as well as machine learning
experiments 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 would need deep
architectures. Deep architectures are composed of multiple levels
of non-linear operations, such as in neural nets with many hidden
layers, graphical models with many levels of latent variables, or
in complicated propositional formulae re-using many sub-formulae.
Each level of the architecture represents features at a different
level of abstraction, defined as a composition of lower-level
features. Searching the parameter space of deep architectures is a
difficult task, but new algorithms have been discovered and a new
sub-area has emerged in the machine learning community since 2006,
following these discoveries. Learning Deep Architectures for AI
discusses the motivations for and principles of learning algorithms
for deep architectures. By analyzing and comparing recent results
with different learning algorithms for deep architectures,
explanations for their success are proposed and discussed,
highlighting challenges and suggesting avenues for future
explorations in this area.
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