Optimization Algorithms for Distributed Machine Learning (Record no. 84778)
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| 000 -LEADER | |
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| fixed length control field | 03324nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-19067-4 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163609.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221125s2023 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031190674 |
| -- | 978-3-031-19067-4 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 518.1 |
| 100 1# - AUTHOR NAME | |
| Author | Joshi, Gauri. |
| 245 10 - TITLE STATEMENT | |
| Title | Optimization Algorithms for Distributed Machine Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 127 p. 40 illus., 38 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Learning, Networks, and Algorithms, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Distributed Optimization in Machine Learning -- Calculus, Probability and Order Statistics Review -- Convergence of SGD and Variance-Reduced Variants -- Synchronous SGD and Straggler-Resilient Variants -- Asynchronous SGD and Staleness-Reduced Variants -- Local-update and Overlap SGD -- Quantized and Sparsified Distributed SGD -- Decentralized SGD and its Variants. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-19067-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2023. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Distribution (Probability theory). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithms. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Design and Analysis of Algorithms. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Distribution Theory. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2690-4314 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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