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This textbook establishes a theoretical framework for understanding
deep learning models of practical relevance. With an approach that
borrows from theoretical physics, Roberts and Yaida provide clear
and pedagogical explanations of how realistic deep neural networks
actually work. To make results from the theoretical forefront
accessible, the authors eschew the subject's traditional emphasis
on intimidating formality without sacrificing accuracy.
Straightforward and approachable, this volume balances detailed
first-principle derivations of novel results with insight and
intuition for theorists and practitioners alike. This
self-contained textbook is ideal for students and researchers
interested in artificial intelligence with minimal prerequisites of
linear algebra, calculus, and informal probability theory, and it
can easily fill a semester-long course on deep learning theory. For
the first time, the exciting practical advances in modern
artificial intelligence capabilities can be matched with a set of
effective principles, providing a timeless blueprint for
theoretical research in deep learning.
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Clergy Retirement (Paperback)
Daniel A. Roberts, Michael Freidman; Edited by Darcy L. Harris
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R619
R558
Discovery Miles 5 580
Save R61 (10%)
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Ships in 18 - 22 working days
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