First-order and Stochastic Optimization Methods for Machine Learning

Author:   Guanghui Lan
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
ISBN:  

9783030395674


Pages:   582
Publication Date:   16 May 2020
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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First-order and Stochastic Optimization Methods for Machine Learning


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Overview

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.

Full Product Details

Author:   Guanghui Lan
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   1.051kg
ISBN:  

9783030395674


ISBN 10:   3030395677
Pages:   582
Publication Date:   16 May 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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