Guanghui Lan

(Author)

First-Order and Stochastic Optimization Methods for Machine Learning (2020)Paperback - 2020, 16 May 2021

First-Order and Stochastic Optimization Methods for Machine Learning (2020)
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Part of Series
Springer the Data Sciences
Print Length
582 pages
Language
English
Publisher
Springer
Date Published
16 May 2021
ISBN-10
3030395707
ISBN-13
9783030395704

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.

Product Details

Author:
Guanghui Lan
Book Edition:
2020
Book Format:
Paperback
Country of Origin:
NL
Date Published:
16 May 2021
Dimensions:
23.39 x 15.6 x 3.07 cm
ISBN-10:
3030395707
ISBN-13:
9783030395704
Language:
English
Location:
Cham
Pages:
582
Publisher:
Weight:
825.54 gm

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