PPT-Scaling Distributed Machine Learning with the Parameter Ser

Author : stefany-barnette | Published Date : 2017-12-29

By M Li D Anderson J Park A Smola A Ahmed V Josifovski J Long E Shekita B Su EECS 582 W16 1 Outline Motivation Parameter Server architecture Why is it special

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Scaling Distributed Machine Learning with the Parameter Ser: Transcript

By M Li D Anderson J Park A Smola A Ahmed V Josifovski J Long E Shekita B Su EECS 582 W16 1 Outline Motivation Parameter Server architecture Why is it special. Andersen Jun Woo Park Alexander J Smola Amr Ahmed Vanja Josifovski James Long Eugene J Shekita BorYiing Su Carnegie Mellon University Baidu Google muli dga junwoop cscmuedu alexsmolaorg amra vanjaj jamlong shekita boryiingsu googlecom Abstract Decision Tree . Ensembles. http://hunch.net/~large_scale_survey. Misha Bilenko (Microsoft) . with Ron . Bekkerman. (LinkedIn) and John Langford (Yahoo!). Rule-based prediction is . natural and powerful (non-linear). . Networks. Distributed. . P. arameter. . Networks. Distributed. . Parameter. . Networks. 1.. The . electric. and . magnetic. . power. . distribute. . homogeneously. . along. . the. . wire. Ashwath Rajan. Overview, in brief. Marriage between statistics, linear algebra, calculus, and computer science. Machine Learning:. Supervised Learning. ex: linear Regression. Unsupervised Learning. ex: clustering. COS 518: Advanced Computer Systems. Lecture . 13. Daniel Suo. Outline. 2. What is machine learning?. Why is machine learning hard in parallel / distributed systems?. A brief history of what people have done. Sebastian . Schelter. , . Venu. . Satuluri. , Reza . Zadeh. Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014. Latent Factor Models. Given . M. sparse. n . x . Corey . Pentasuglia. Masters Project. 5/11/2016. Examiners. Dr. Scott . Spetka. Dr. . Bruno . Andriamanalimanana. Dr. Roger . Cavallo. Masters Project Objectives. Research DML (Distributed Machine Learning). Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Course Project. Write-ups due June 1. st. Project presentations . 12 presentations, 10 mins each, 15 min slack. . 15-213 / 18-213 / 15-513: Introduction to Computer Systems. 28. th. Lecture, December 5, 2017. Today’s Instructor:. . Phil Gibbons. What’s So Special about…Big Data?. Focus of this Talk: Big Learning. Big Learning?. A Distributed Systems Perspective. . Phillip B. Gibbons. Carnegie Mellon University. ICDCS’16 Keynote Talk, June 28, 2016. What’s So Special about…Big Data?. Keynote #2: Prof. Masaru . Big Learning?. A Distributed Systems Perspective. . Phillip B. Gibbons. Carnegie Mellon University. ICDCS’16 Keynote Talk, June 28, 2016. What’s So Special about…Big Data?. Keynote #2: Prof. Masaru . . 15-213 / 18-213 / 15-513: Introduction to Computer Systems. 28. th. Lecture, December 5, 2017. Today’s Instructor:. . Phil Gibbons. What’s So Special about…Big Data?. Focus of this Talk: Big Learning. Sylvia Unwin. Faculty, Program Chair. Assistant Dean, iBIT. Machine Learning. Attended TDWI in Oct 2017. Focus on Machine Learning, Data Science, Python, AI. Started with a catchy opening speech – “BS-Free AI For Business”. Abid M. Malik. Meifeng. Lin (PI). Collaborators: Amir . Farbin. (UT) , Jean . Roch. ( CERN). Computer Science and Mathematic Department. Brookhaven National Laboratory (BNL). Distributed ML for HEP.

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