PPT-Efficient Program Compilation through Machine Learning Techniques

Author : liane-varnes | Published Date : 2018-10-22

Gennady Pekhimenko IBM Canada Angela Demke Brown University of Toronto Motivation My cool program Compiler O2 DCE Peephole Unroll Inline Executable But what to

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Efficient Program Compilation through Machine Learning Techniques: Transcript

Gennady Pekhimenko IBM Canada Angela Demke Brown University of Toronto Motivation My cool program Compiler O2 DCE Peephole Unroll Inline Executable But what to do if executable is slow. Kakade SKAKADE MICROSOFT COM Microsoft Research New England One Memorial Drive Cambridge MA 02142 USA Shai ShalevShwartz SHAIS CS HUJI AC IL School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem 91904 Isra Programming Language Pragmatics. Michael L. Scott. Programming Languages. What programming languages can you name?. Which do you know?. Introduction. Why are there so many programming languages?. evolution -- we've learned better ways of doing things over time. 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. R/Finance. 20 May 2016. Rishi K Narang, Founding Principal, T2AM. What the hell are we talking about?. What the hell is machine learning?. How the hell does it relate to investing?. Why the hell am I mad at it?. David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. Programming Language Pragmatics. Michael L. Scott. Programming Languages. What programming languages can you name?. Which do you know?. Introduction. Why are there so many programming languages?. evolution -- we've learned better ways of doing things over time. An Overview of Machine Learning Speaker: Yi-Fan Chang Adviser: Prof. J. J. Ding Date : 2011/10/21 What is machine learning ? Learning system model Training and testing Performance Algorithms Machine learning Improving Predictive Models with Machine Learning & Big Data. Predictive Modeling in Healthcare -. . Why Predict? . Use Cases: Existing Predictive . M. odeling . T. echniques. Reducing Preventable. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Gihyuk Ko. PhD Student, Department of Electrical and Computer Engineering. Carnegie Mellon University. November. 14, 2016. *some slides were borrowed from . Anupam. . Datta’s. MIT Big . Data@CSAIL. 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”.

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