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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) - Hardcover
- PublisherSpringer
- Publication date2020
- ISBN 10 3030395677
- ISBN 13 9783030395674
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages595
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First-Order and Stochastic Optimization Methods for Machine Learning
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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 2020, 2020
ISBN 10: 3030395677
ISBN 13: 9783030395674
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Buch. 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 # 9783030395674
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First-Order and Stochastic Optimization Methods for Machine Learning
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
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