Distributed Machine Learning and Gradient Optimization
暫譯: 分散式機器學習與梯度優化

Jiang, Jiawei, Cui, Bin, Zhang, Ce

Distributed Machine Learning and Gradient Optimization
  • 出版商: Springer
  • 出版日期: 2022-02-24
  • 售價: $6,780
  • 貴賓價: 9.5 折 $6,441
  • 語言: 英文
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 981163419X
  • ISBN-13: 9789811634192
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.

Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

商品描述(中文翻譯)

本書介紹了基於梯度優化方法的分散式機器學習演算法的最新技術。在大數據時代，大規模數據集對現有的機器學習系統提出了巨大的挑戰。因此，在分散式環境中實現機器學習演算法已成為一項關鍵技術，最近的研究顯示基於梯度的迭代優化是一個有效的解決方案。本書專注於通過演算法優化和仔細的系統實現來加速大規模梯度優化的方法，介紹了設計梯度優化演算法以訓練分散式機器學習模型的三個基本技術：平行策略、數據壓縮和同步協議。

本書以教學風格撰寫，涵蓋了從基本知識到多個精心設計的分散式機器學習演算法和系統的各種主題。它將吸引機器學習、人工智慧、大數據和數據庫管理領域的廣泛讀者。

作者簡介

Jiawei Jiang obtained his PhD from Peking University 2018, advised by Prof. Bin Cui. His research interests include distributed machine learning, gradient optimization and automatic machine learning. He has served as a program committee member or reviewer for various international events, including SIGMOD, VLDB, ICDE, KDD, AAAI and TKDE. He was awarded the CCF Outstanding Doctoral Dissertation Award (2019) and ACM China Doctoral Dissertation Award (2018).

Bin Cui is a Professor at the School of EECS and Director of the Institute of Network Computing and Information Systems, at Peking University. His research interests include database system architectures, query and index techniques, and big data management and mining. He has published over 200 refereed papers at international conferences and in journals. Dr. Cui has served on the technical program committee of various international conferences, including SIGMOD, VLDB, ICDE and KDD, and as Vice PC Chair of ICDE 2011, Demo Co-Chair of ICDE 2014, Area Chair of VLDB 2014, PC Co-Chair of APWeb 2015 and WAIM 2016. He is currently a member of the trustee board of VLDB Endowment, is on the editorial board of the VLDB Journal, Distributed and Parallel Databases Journal, and Information Systems, and was formerly an associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE, 2009-2013). He was selected for a Microsoft Young Professorship award (MSRA 2008), CCF Young Scientist award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed a Cheung Kong distinguished Professor by the MOE in 2016.

 

作者簡介(中文翻譯)

江家偉於2018年獲得北京大學博士學位，指導教授為崔斌教授。他的研究興趣包括分散式機器學習、梯度優化和自動化機器學習。他曾擔任多個國際會議的程序委員會成員或審稿人，包括SIGMOD、VLDB、ICDE、KDD、AAAI和TKDE。他獲得了CCF優秀博士論文獎（2019年）和ACM中國博士論文獎（2018年）。

崔斌是北京大學電子工程與計算機科學學院的教授，並擔任網路計算與資訊系統研究所所長。他的研究興趣包括資料庫系統架構、查詢和索引技術，以及大數據管理和挖掘。他在國際會議和期刊上發表了超過200篇經過審核的論文。崔博士曾擔任多個國際會議的技術程序委員會成員，包括SIGMOD、VLDB、ICDE和KDD，並於2011年擔任ICDE的副程序主席、2014年ICDE的示範共同主席、2014年VLDB的區域主席、2015年APWeb和2016年WAIM的程序共同主席。他目前是VLDB基金會的受託人委員會成員，並在VLDB期刊、分散式與平行資料庫期刊和資訊系統的編輯委員會中任職，曾於2009年至2013年擔任IEEE知識與資料工程學報（TKDE）的副編輯。他曾獲得微軟青年教授獎（MSRA 2008）、CCF青年科學家獎（2009）、中國教育部自然科學獎二等獎（2014），並於2016年被教育部聘為長江學者特聘教授。

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