First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)

ISBN-13: 9783030395674
ISBN-10: 3030395677
Edition: 1st ed. 2020
Author: Lan, Guanghui
Publication date: 2020
Publisher: Springer
Format: Hardcover 595 pages
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Book details

ISBN-13: 9783030395674
ISBN-10: 3030395677
Edition: 1st ed. 2020
Author: Lan, Guanghui
Publication date: 2020
Publisher: Springer
Format: Hardcover 595 pages

Summary

Acknowledged authors Lan, Guanghui wrote First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) comprising 595 pages back in 2020. Textbook and eTextbook are published under ISBN 3030395677 and 9783030395674. Since then First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

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.


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