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Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
De la contraportada
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)
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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. Condición: 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. Nº de ref. del artículo: 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
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First-Order and Stochastic Optimization Methods for Machine Learning
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Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 41454145
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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
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Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677
ISBN 13: 9783030395674
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Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Hardback. Condición: 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. Nº de ref. del artículo: LU-9783030395674
Cantidad disponible: Más de 20 disponibles
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First-order and Stochastic Optimization Methods for Machine Learning
Publicado por
Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030395677
ISBN 13: 9783030395674
Nuevo
Tapa dura
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Hardback. Condición: 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. Nº de ref. del artículo: LU-9783030395674
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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)
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