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Deep Learning (Hardcover)
Ian Goodfellow, Yoshua Bengio, Aaron Courville
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R2,887
R2,529
Discovery Miles 25 290
Save R358 (12%)
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Ships in 9 - 15 working days
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An introduction to a broad range of topics in deep learning,
covering mathematical and conceptual background, deep learning
techniques used in industry, and research perspectives. "Written by
three experts in the field, Deep Learning is the only comprehensive
book on the subject." -Elon Musk, cochair of OpenAI; cofounder and
CEO of Tesla and SpaceX Deep learning is a form of machine learning
that enables computers to learn from experience and understand the
world in terms of a hierarchy of concepts. Because the computer
gathers knowledge from experience, there is no need for a human
computer operator to formally specify all the knowledge that the
computer needs. The hierarchy of concepts allows the computer to
learn complicated concepts by building them out of simpler ones; a
graph of these hierarchies would be many layers deep. This book
introduces a broad range of topics in deep learning. The text
offers mathematical and conceptual background, covering relevant
concepts in linear algebra, probability theory and information
theory, numerical computation, and machine learning. It describes
deep learning techniques used by practitioners in industry,
including deep feedforward networks, regularization, optimization
algorithms, convolutional networks, sequence modeling, and
practical methodology; and it surveys such applications as natural
language processing, speech recognition, computer vision, online
recommendation systems, bioinformatics, and videogames. Finally,
the book offers research perspectives, covering such theoretical
topics as linear factor models, autoencoders, representation
learning, structured probabilistic models, Monte Carlo methods, the
partition function, approximate inference, and deep generative
models. Deep Learning can be used by undergraduate or graduate
students planning careers in either industry or research, and by
software engineers who want to begin using deep learning in their
products or platforms. A website offers supplementary material for
both readers and instructors.
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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