First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)
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First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)

by Guanghui Lan

Mathematics Machine Learning Data Science
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Explores first-order and stochastic optimization methods for machine learning, providing theoretical foundations and practical approaches within the Springer Series in the Data Sciences.

ISBN-13 9783030395674 ASIN 3030395677 Views 51 Publisher Springer Year 2020 Edition 2020 ed. Format Hardcover Product Group Book Pages 595 Created March 15, 2026 Updated October 06, 2026
About This Book

This book presents first-order and stochastic optimization methods for machine learning.

It covers theoretical foundations and practical approaches in optimization.

The content is part of the Springer Series in the Data Sciences.

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I will be using this book for: