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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Non-convex Optimization for Machine Learning (Paperback)
Loot Price: R2,374
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Non-convex Optimization for Machine Learning (Paperback)
Series: Foundations and Trends in Machine Learning
Expected to ship within 10 - 15 working days
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Non-convex Optimization for Machine Learning takes an in-depth look
at the basics of non-convex optimization with applications to
machine learning. It introduces the rich literature in this area,
as well as equips the reader with the tools and techniques needed
to apply and analyze simple but powerful procedures for non-convex
problems. Non-convex Optimization for Machine Learning is as
self-contained as possible while not losing focus of the main topic
of non-convex optimization techniques. The monograph initiates the
discussion with entire chapters devoted to presenting a
tutorial-like treatment of basic concepts in convex analysis and
optimization, as well as their non-convex counterparts. The
monograph concludes with a look at four interesting applications in
the areas of machine learning and signal processing, and exploring
how the non-convex optimization techniques introduced earlier can
be used to solve these problems. The monograph also contains, for
each of the topics discussed, exercises and figures designed to
engage the reader, as well as extensive bibliographic notes
pointing towards classical works and recent advances. Non-convex
Optimization for Machine Learning can be used for a semester-length
course on the basics of non-convex optimization with applications
to machine learning. On the other hand, it is also possible to
cherry pick individual portions, such the chapter on sparse
recovery, or the EM algorithm, for inclusion in a broader course.
Several courses such as those in machine learning, optimization,
and signal processing may benefit from the inclusion of such
topics.
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