First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) - Hardcover

Buch 5 von 11: Springer Series in the Data Sciences

Lan, Guanghui

 
9783030395674: First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Hardcover
ISBN 10: 3030395677 ISBN 13: 9783030395674
Verlag: Springer, 2020

Inhaltsangabe

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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Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2020
Sprache
Englisch
ISBN 10 
3030395677
ISBN 13 
9783030395674
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
595
Kontakt zum Hersteller
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Heidelberg
69121
Deutschland

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9783030395704: First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)

Vorgestellte Ausgabe

ISBN 10:  3030395707 ISBN 13:  9783030395704
Verlag: Springer, 2021
Softcover