| 書目名稱 | First-order and Stochastic Optimization Methods for Machine Learning |
| 編輯 | Guanghui Lan |
| 視頻video | http://file.papertrans.cn/344/343879/343879.mp4 |
| 概述 | 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 br |
| 叢書名稱 | Springer Series in the Data Sciences |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | Stochastic optimization methods; Machine learning algorithms; Randomized algorithms; Nonconvex optimiza |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-39568-1 |
| isbn_softcover | 978-3-030-39570-4 |
| isbn_ebook | 978-3-030-39568-1Series ISSN 2365-5674 Series E-ISSN 2365-5682 |
| issn_series | 2365-5674 |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機(jī)版|小黑屋|
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