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Springer Series in the Data Sciences

First-order and Stochastic Optimization Methods for Machine Learning

Authors: Lan, George

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  • Presents comprehensive study of topics in machine learning from introductory material through most complicated algorithms
  • Summarizes most recent findings in the area of machine learning
  • Addresses a broad audience in machine learning, artificial intelligence, and mathematical programming
  • Includes exercises
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eBook $109.00
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  • ISBN 978-3-030-39568-1
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Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-39567-4
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  • Immediate ebook access, if available*, with your print order
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About this book

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.



Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-39568-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-39567-4
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
First-order and Stochastic Optimization Methods for Machine Learning
Authors
Series Title
Springer Series in the Data Sciences
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-39568-1
DOI
10.1007/978-3-030-39568-1
Hardcover ISBN
978-3-030-39567-4
Series ISSN
2365-5674
Edition Number
1
Number of Pages
XIII, 582
Number of Illustrations
2 b/w illustrations, 16 illustrations in colour
Topics

*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.