Description: First-Order and Stochastic Optimization Methods for Machine Learning, Hardcover by Lan, Guanghui, ISBN 3030395677, ISBN-13 9783030395674, Brand New, 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.
Price: 125.75 USD
Location: Jessup, Maryland
End Time: 2024-08-02T22:27:12.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 14 Days
Refund will be given as: Money Back
Return policy details:
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
