First-order and Stochastic Optimization Methods for Machine Learning
Language: English
Published by Springer Nature Switzerland AG, CH, 2020
- Hardcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
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Condition: New
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Add to basketItem description from seller
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.
Seller Inventory # LU-9783030395674
- Title
- First-order and Stochastic Optimization Methods for Machine Learning
- Author
- Guanghui Lan
- Publisher
- Springer Nature Switzerland AG, CH
- Publication year
- 2020
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 3030395677
- ISBN 13
- 9783030395674
- Edition
- 2020 ed.
- Dimensions
- 16.51 x 3.81 x 23.5 cm
- Series
- Book 5 of 11: Springer Series in the Data Sciences
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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