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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPrices, Columbia, MD, U.S.A.
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First-Order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPrices, Columbia, MD, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Libro 5 di 11: Springer Series in the Data SciencesDa: Ria Christie Collections, Uxbridge, Regno Unito
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Libro 5 di 11: Springer Series in the Data SciencesDa: Ria Christie Collections, Uxbridge, Regno Unito
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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
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPricesUK, Woodford Green, Regno Unito
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
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Aggiungi al carrelloPaperback. Condizione: 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
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPrices, Columbia, MD, U.S.A.
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First-Order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPricesUK, Woodford Green, Regno Unito
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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
Libro 5 di 11: Springer Series in the Data SciencesDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Numerical Methods for Convex Multistage Stochastic Optimization (Foundations and Trends(r) in Optimization)
Da: Books Puddle, New York, NY, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloHardback. Condizione: 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 (Springer Series in the Data Sciences) 1st ed. 2020 Edition
Libro 5 di 11: Springer Series in the Data SciencesDa: Books Puddle, New York, NY, U.S.A.
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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
Libro 5 di 11: Springer Series in the Data SciencesDa: Biblios, Frankfurt am main, HESSE, Germania
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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)
Libro 5 di 11: Springer Series in the Data SciencesDa: Lucky's Textbooks, Dallas, TX, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: 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 (Springer Series in the Data Sciences)
Libro 5 di 11: Springer Series in the Data SciencesDa: California Books, Miami, FL, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
Libro 5 di 11: Springer Series in the Data SciencesDa: California Books, Miami, FL, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloHardback. Condizione: 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 (Springer Series in the Data Sciences)
Libro 5 di 11: Springer Series in the Data SciencesDa: Mispah books, Redhill, SURRE, Regno Unito
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First-order And Stochastic Optimization
Libro 5 di 11: Springer Series in the Data SciencesDa: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
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Aggiungi al carrelloTaschenbuch. Condizione: 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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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer International Publishing, 2020
ISBN 10: 3030395677 ISBN 13: 9783030395674
EUR 149,79
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Aggiungi al carrelloBuch. Condizione: 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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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesDa: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 595 pages. 9.25x6.10x1.21 inches. In Stock.
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First-Order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesDa: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: Brand New. 582 pages. 9.25x6.50x1.50 inches. In Stock.
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First-order And Stochastic Optimization
Libro 5 di 11: Springer Series in the Data SciencesDa: Kennys Bookstore, Olney, MD, U.S.A.
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First-order and Stochastic Optimization Methods for Machine Learning
Libro 5 di 11: Springer Series in the Data SciencesLingua: Inglese
Editore: Springer International Publishing Mai 2021, 2021
ISBN 10: 3030395707 ISBN 13: 9783030395704
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
