| Tag |
In 1 |
In 2 |
Data |
| 001 | | | vtls000035763 |
| 003 | | | IISER-K |
| 005 | | | 20211008113400.0 |
| 007 | | | cr nn 008mamaa |
| 008 | | | 211008s2020 sz | s |||| 0|eng d |
| 020 | | | \a 9783030395681 \9 978-3-030-39568-1 |
| 035 | | | \a (DE-He213)978-3-030-39568-1 |
| 039 | | 9 | \y 202110081134 \z Siladitya |
| 050 | | 4 | \a QA402.5-402.6 |
| 082 | 0 | 4 | \a 519.6 \2 23 |
| 100 | 1 | | \a Lan, Guanghui. \e author. \4 aut \4 http://id.loc.gov/vocabulary/relators/aut |
| 245 | 1 | 0 | \a First-order and Stochastic Optimization Methods for Machine Learning \h [electronic resource] / \c by Guanghui Lan. |
| 250 | | | \a 1st ed. 2020. |
| 264 | | 1 | \a Cham : \b Springer International Publishing : \b Imprint: Springer, \c 2020. |
| 300 | | | \a XIII, 582 p. 18 illus., 16 illus. in color. \b online resource. |
| 336 | | | \a text \b txt \2 rdacontent |
| 337 | | | \a computer \b c \2 rdamedia |
| 338 | | | \a online resource \b cr \2 rdacarrier |
| 347 | | | \a text file \b PDF \2 rda |
| 490 | 1 | | \a Springer Series in the Data Sciences, \x 2365-5682 |
| 505 | 0 | | \a Machine Learning Models -- Convex Optimization Theory -- Deterministic Convex Optimization -- Stochastic Convex Optimization -- Convex Finite-sum and Distributed Optimization -- Nonconvex Optimization -- Projection-free Methods -- Operator Sliding and Decentralized Optimization. |
| 520 | | | \a 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. |
| 650 | | 0 | \a Mathematical optimization. |
| 650 | | 0 | \a Machine learning. |
| 650 | 1 | 4 | \a Optimization. \0 https://scigraph.springernature.com/ontologies/product-market-codes/M26008 |
| 650 | 2 | 4 | \a Machine learning. \0 https://scigraph.springernature.com/ontologies/product-market-codes/I21010 |
| 710 | 2 | | \a SpringerLink (Online service) |
| 773 | 0 | | \t Springer Nature eBook |
| 776 | 0 | 8 | \i Printed edition: \z 9783030395674 |
| 776 | 0 | 8 | \i Printed edition: \z 9783030395698 |
| 776 | 0 | 8 | \i Printed edition: \z 9783030395704 |
| 830 | | 0 | \a Springer Series in the Data Sciences, \x 2365-5682 |
| 856 | 4 | 0 | \u https://doi.org/10.1007/978-3-030-39568-1 |
| 912 | | | \a ZDB-2-SMA |
| 912 | | | \a ZDB-2-SXMS |
| 950 | | | \a Mathematics and Statistics (SpringerNature-11649) |
| 950 | | | \a Mathematics and Statistics (R0) (SpringerNature-43713) |
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