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Optimization for learning and control /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization for learning and control // Anders Hansson, Martin Andersen.
作者:	Hansson, Anders.
其他作者:	Andersen, Martin S.
出版者:	Hoboken, NJ :Wiley, : c2023.,
面頁冊數:	xxvii, 397 p. :ill. ; : 26 cm.;
標題:	System analysis - Mathematics. -
ISBN:	9781119809135

Optimization for learning and control /
Hansson, Anders.

Optimization for learning and control /Anders Hansson, Martin Andersen. - Hoboken, NJ :Wiley,c2023. - xxvii, 397 p. :ill. ;26 cm.

Includes bibliographical references and index.

"Comprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems.Â Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters' level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters' students in a coherent way"--

ISBN: 9781119809135NT3979

LCCN: 2023002568Subjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

997150
System analysis
--Mathematics.

LC Class. No.: T57.62 / .H43 2023

Dewey Class. No.: 004.2/10151 / H251 2023

Optimization for learning and control /
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504 $a Includes bibliographical references and index.
520 # $a "Comprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems.Â Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters' level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters' students in a coherent way"-- $c Provided by publisher.
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650 # 0 $a Mathematical optimization. $3 527675
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