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20150625-19 陈天奇:Replicable Parts for Large-scale Deep Learning

2015-6-23 22:01| 发布者: 郑海永海大| 查看: 6176| 评论: 0

摘要: 报告嘉宾2:陈天奇(University of Washington)主持人:王乃岩(香港科技大学)报告时间:2015年6月25日中午13:00(北京时间)报告题目:Replicable Parts for Large-scale Deep Learning 文章信息:报告摘要:In t ...
报告嘉宾2:陈天奇(University of Washington) 
主持人:王乃岩(香港科技大学) 
报告时间:2015年6月25日中午13:00(北京时间) 
报告题目:Replicable Parts for Large-scale Deep Learning http://valser.org/webinar/slide/slides/20150625/VALSE20150625_ChenTianQi.pptx
报告摘要:In this talk, I will introduce the lessons we learned in DMLC to develop large-scale (deep) machine learning toolkits. Specifically, I will discuss how the problem of building such system can be decomposed into small essential parts, and how these parts can combined together to make the entire code-base more concise, flexible and fast. This talk will cover the topics on tensor expression, parameter server synchronization and operation scheduling. I will also briefly talk about distributed data loading API if time permits. 
报告人简介:Tianqi is a PhD student at University of Washington, working on Large-scale machine learning. He has expertise in both machine learning theory and engineering. He has been publishing papers in top machine learning conferences. He is main contributor of several popular open-source machine learning packages, including xgboost, cxxnet, mshadow. He was also winner of two KDDCup challenges.

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