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First-order and stochastic optimization methods for machine learning / by Lan Guanghui

By: Material type: TextTextLanguage: English Series: Springer series in the data sciencesPublication details: Switzerland, Springer: 2020Description: xiii, 582p. : illISBN:
  • 9783030395674
Subject(s): Other classification:
  • B2811093 R0
Summary: 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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Item type Current library Home library Call number Status Barcode
Textual Textual Central Science Library Central Science Library B2811093 R0 (Browse shelf(Opens below)) Available SL1654757

References 567-575p.; Index 577-582p.

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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