PPT-Machine Learning with MapReduce

Author : tatiana-dople | Published Date : 2016-05-19

KMeans Clustering 3 How to MapReduce KMeans Given K assign the first K random points to be the initial cluster centers Assign subsequent points to the closest

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Machine Learning with MapReduce: Transcript

KMeans Clustering 3 How to MapReduce KMeans Given K assign the first K random points to be the initial cluster centers Assign subsequent points to the closest cluster using the supplied distance measure. Simplified Data Processing on Large . Clusters. by Jeffrey Dean and Sanjay . Ghemawa. Presented by Jon Logan. Outline. Problem Statement / Motivation. An Example Program. MapReduce. . vs. Hadoop. GFS / HDFS. Clustering and pattern recognition. W. ikipedia entry on machine learning. 7.1 Decision tree learning. 7.2 Association rule learning. 7.3 Artificial neural networks. 7.4 Genetic programming. 7.5 Inductive logic programming. Lecture . 4. Multilayer . Perceptrons. G53MLE | Machine Learning | Dr Guoping Qiu. 1. Limitations of Single Layer Perceptron. Only express linear decision surfaces. G53MLE | Machine Learning | Dr Guoping Qiu. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . Presented By. Shefali. . Gundecha. Srinivas . Narne. Yash. Kulkarni. Papers to be discussed…. Y. Shan, B. Wang, J. Yan, Y. Wang, N. Xu, and H. Yang, . " FPMR: MapReduce Framework on FPGA: A Case Study of . Yasin N. Silva and Jason Reed. Arizona State University. 1. This work is licensed under a Creative Commons Attribution-. NonCommercial. -. ShareAlike. 4.0 International License. See http://creativecommons.org/licenses/by-nc-sa/4.0/ for details.. : Simplified Data Processing on Large Clusters . Jeff Dean, Sanjay . Ghemawat. , Google, OSDI 2004. Slides based on those by . authors and other online sources. Motivation. Large . scale data processing. Prabhat. Data Day. August 22, 2016. Roadmap. Why you should care about Machine Learning?. Trends in Industry. Trends in Science . What is Machine Learning?. Taxonomy. Methods. Tools (Evan . Racah. ). Sixth International Workshop on Cloud Data . Management. CloudDB. 2014. Chicago March 31 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Implications . for . Software Environments. . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. ”. Cathy O’Neil & Rachel . Schutt. , 2013. R & Hadoop. Compute squares. 2. R. # create a list of 10 integers. ints. <- 1:10. # equivalent to . ints. <- c(1,2,3,4,5,6,7,8,9,10). # compute the squares. Jimmy Lin. The iSchool. University of Maryland. Monday, March 30, 2009. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.

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