| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433236 |
| 005 | | 20200225115228 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085783422 |
| 035 | |
▼a (MiAaPQ)AAI13882920 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001 |
| 100 | 1 |
▼a Nishihara, Robert K. |
| 245 | 10 |
▼a On Systems and Algorithms for Distributed Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 113 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: B. |
| 500 | |
▼a Advisor: Jordan, Michael I. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a The advent of algorithms capable of leveraging vast quantities of data and computational resources has led to the proliferation of systems and tools aimed to facilitate the development and usage of these algorithms. Hardware trends, including the end of Moore's Law and the maturation of cloud computing, have placed a premium on the development of scalable algorithms designed for parallel architectures. The combination of these factors has made distributed computing an integral part of machine learning in practice.This thesis examines the design of systems and algorithms to support machine learning in the distributed setting. The distributed computing landscape today consists of many domain-specific tools. We argue that these tools underestimate the generality of many modern machine learning applications and hence struggle to support them. We examine the requirements of a system capable of supporting modern machine learning workloads and present a general-purpose distributed system architecture for doing so. In addition, we examine several examples of specific distributed learning algorithms. We explore the theoretical properties of these algorithms and see how they can leverage such a system. |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Electrical Engineering & Computer Sciences. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491265
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |