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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) - Softcover
- PublisherSpringer
- Publication date2021
- ISBN 10 3030395707
- ISBN 13 9783030395704
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages595
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
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First-order and Stochastic Optimization Methods for Machine Learning
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Springer International Publishing Mai 2021, 2021
ISBN 10: 3030395707
ISBN 13: 9783030395704
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 596 pp. Englisch. Seller Inventory # 9783030395704
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First-order and Stochastic Optimization Methods for Machine Learning
Published by
Springer International Publishing, 2021
ISBN 10: 3030395707
ISBN 13: 9783030395704
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Taschenbuch
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783030395704
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FIRST ORDER AND STOCHASTIC OPTIMIZATION METHODS FOR MACHINE LEARNING (PB 2020)
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
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First-order and Stochastic Optimization Methods for Machine Learning
Published by
Springer International Publishing, 2021
ISBN 10: 3030395707
ISBN 13: 9783030395704
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents comprehensive study of topics in machine learning from introductory material through most complicated algorithmsSummarizes most recent findings in the area of machine learningAddresses a broad audience . Seller Inventory # 466230490
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) 1st ed. 2020 Edition
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) 1st ed. 2020 Edition
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) 1st ed. 2020 Edition
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