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First Order Stochastic Optimization Methods by Lan Guanghui (39 results)
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Book 5 of 11: Springer Series in the Data SciencesSeller: Ria Christie Collections, Uxbridge, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Book 5 of 11: Springer Series in the Data SciencesSeller: Ria Christie Collections, Uxbridge, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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First-Order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
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First-Order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
US$ 107.05
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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 (Hardcover)
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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First-order and Stochastic Optimization Methods for Machine Learning (Paperback)
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
-
First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
US$ 126.13
US$ 88.98 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.
-
First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Book 5 of 11: Springer Series in the Data SciencesUS$ 188.94
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Add to basketHardcover. Condition: New. New. book.
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First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesLanguage: 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
Book 5 of 11: Springer Series in the Data SciencesLanguage: 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.
-
First-order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesUS$ 258.87
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Add to basketPaperback. Condition: Brand New. 595 pages. 9.25x6.10x1.21 inches. In Stock.
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First-Order and Stochastic Optimization Methods for Machine Learning
Book 5 of 11: Springer Series in the Data SciencesUS$ 261.28
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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 (Paperback)
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
-
First-order and Stochastic Optimization Methods for Machine Learning (Hardcover)
Book 5 of 11: Springer Series in the Data SciencesLanguage: English
Published by Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
