First-Order and Stochastic Optimization Methods for Machine Learning, Hardcov...

Description: First-Order and Stochastic Optimization Methods for Machine Learning, Hardcover by Lan, Guanghui, ISBN 3030395677, ISBN-13 9783030395674, Like New Used, Free shipping in the US

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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End Time: 2024-08-02T22:27:12.000Z

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Book Title: First-Order and Stochastic Optimization Methods for Machine Learn

Number of Pages: Xiii, 582 Pages

Publication Name: First-Order and Stochastic Optimization Methods for Machine Learning

Language: English

Publisher: Springer International Publishing A&G

Subject: Intelligence (Ai) & Semantics, Probability & Statistics / General, Optimization

Publication Year: 2020

Item Weight: 37.1 Oz

Type: Textbook

Subject Area: Mathematics, Computers

Item Length: 9.3 in

Author: Guanghui Lan

Series: Springer Series in the Data Sciences Ser.

Item Width: 6.1 in

Format: Hardcover