First-order and Stochastic Optimization Methods for Machine Learning
Series: Springer Series in the Data Sciences;
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63 540 Ft
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Product details:
- Edition number 1st ed. 2020
- Publisher Springer
- Date of Publication 16 May 2020
- Number of Volumes 1 pieces, Book
- ISBN 9783030395674
- Binding Hardback
- No. of pages582 pages
- Size 235x155 mm
- Weight 1051 g
- Language English
- Illustrations 2 Illustrations, black & white; 16 Illustrations, color 112
Categories
Short description:
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.
MoreLong description:
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.
Table of Contents:
Machine Learning Models.- Convex Optimization Theory.- Deterministic Convex Optimization.- Stochastic Convex Optimization.- Convex Finite-sum and Distributed Optimization.- Nonconvex Optimization.- Projection-free Methods.- Operator Sliding and Decentralized Optimization.
More
First-order and Stochastic Optimization Methods for Machine Learning
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