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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Optimization for Data Analysis (Hardcover)
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Optimization for Data Analysis (Hardcover)
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Optimization techniques are at the core of data science, including
data analysis and machine learning. An understanding of basic
optimization techniques and their fundamental properties provides
important grounding for students, researchers, and practitioners in
these areas. This text covers the fundamentals of optimization
algorithms in a compact, self-contained way, focusing on the
techniques most relevant to data science. An introductory chapter
demonstrates that many standard problems in data science can be
formulated as optimization problems. Next, many fundamental methods
in optimization are described and analyzed, including: gradient and
accelerated gradient methods for unconstrained optimization of
smooth (especially convex) functions; the stochastic gradient
method, a workhorse algorithm in machine learning; the coordinate
descent approach; several key algorithms for constrained
optimization problems; algorithms for minimizing nonsmooth
functions arising in data science; foundations of the analysis of
nonsmooth functions and optimization duality; and the
back-propagation approach, relevant to neural networks.
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