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Optimization for data analysis
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Optimization for data analysis/ Stephen J. Wright, Benjamin Recht.
作者:
Wright, Stephen J.
其他作者:
Recht, Benjamin.
出版者:
Cambridge :Cambridge University Press, : 2022.,
面頁冊數:
x, 227 p. :ill., digital ; : 24 cm.;
附註:
Title from publisher's bibliographic system (viewed on 01 Apr 2022).
標題:
Artificial intelligence. -
電子資源:
https://doi.org/10.1017/9781009004282
ISBN:
9781009004282
Optimization for data analysis
Wright, Stephen J.
Optimization for data analysis
[electronic resource] /Stephen J. Wright, Benjamin Recht. - Cambridge :Cambridge University Press,2022. - x, 227 p. :ill., digital ;24 cm.
Title from publisher's bibliographic system (viewed on 01 Apr 2022).
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.
ISBN: 9781009004282Subjects--Topical Terms:
559380
Artificial intelligence.
LC Class. No.: QA76.9.B45 / W75 2022
Dewey Class. No.: 005.7
Optimization for data analysis
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https://doi.org/10.1017/9781009004282
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