Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
First-Order and Stochastic Optimization Methods for Machine Learning

First-Order and Stochastic Optimization Methods for Machine Learning

Lan, Guanghui

Hardcover

Series: Springer the Data Sciences

General ComputersGeneral Mathematics

ISBN10: 3030395677
ISBN13: 9783030395674
Publisher: Springer
Published: May 16 2020
Pages: 582
Weight: 2.22
Height: 1.31 Width: 6.14 Depth: 9.21
Language: English

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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