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Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
US$ 107.72
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Add to basketPaperback. Condition: New. 2020 ed. 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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First-order and Stochastic Optimization Methods for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 95.07
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First-Order and Stochastic Optimization Methods for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 95.07
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First-order and Stochastic Optimization Methods for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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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
Language: English
Published by Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
US$ 126.60
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Add to basketHardback. Condition: New. 2020 ed. 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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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
US$ 104.53
US$ 86.89 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback. Condition: New. 2020 ed. 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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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
US$ 123.17
US$ 86.89 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. 2020 ed. 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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Add to basketHardcover. Condition: New. New. book.
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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer International Publishing, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Taschenbuch. Condition: Neu. First-order and Stochastic Optimization Methods for Machine Learning | Guanghui Lan | Taschenbuch | xiii | Englisch | 2021 | Springer International Publishing | EAN 9783030395704 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer International Publishing, Springer International Publishing, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
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.
-
First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer International Publishing, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Buch. 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.
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US$ 260.55
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Add to basketPaperback. Condition: Brand New. 595 pages. 9.25x6.10x1.21 inches. In Stock.
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US$ 262.97
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Add to basketHardcover. Condition: Brand New. 582 pages. 9.25x6.50x1.50 inches. In Stock.
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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer International Publishing Mai 2021, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
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.
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First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer International Publishing Mai 2020, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
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.
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US$ 124.43
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Add to basketCondition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
