First-order and Stochastic Optimization Methods for Machine Learning

Lieferzeit: Lieferbar innerhalb 14 Tagen

149,79 

Springer Series in the Data Sciences

ISBN: 3030395707
ISBN 13: 9783030395704
Autor: Lan, Guanghui
Verlag: Springer Verlag GmbH
Umfang: xiii, 582 S., 2 s/w Illustr., 16 farbige Illustr., 582 p. 18 illus., 16 illus. in color.
Erscheinungsdatum: 16.05.2021
Auflage: 1/2021
Produktform: Kartoniert
Einband: Kartoniert

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Artikelnummer: 2076701 Kategorie:

Beschreibung

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

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