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Machine Learning vs Clojure
Transcript of Machine Learning vs Clojure
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Convert to Doubles Data Pipelines Advanced Pipelines Storm Hadoop Cascalog Matrix Multiplication http://upload.wikimedia.org/wikipedia/en/e/eb/Matrix_multiplication_diagram_2.svg Matrix Multiplication Performance Performance Vectorized vs Multi-Threaded Multiply 1000x1000 matrix of doubles Intel(R) Core(TM)2 Quad CPU Q9400 @ 2.66GHz Naive impl: 220s
Vectorized(clatrix/jblas): 0.8 s 2nd Order MM, 100,000 words P(3rd|1st,2nd) needs 10^15 values * * GPGPU on the JVM soon? aparapi - converts java byte code to Open CL to run on GPU (not clojure) Project Sumatra - generalised API for tuples / GPGPU on JVM Matrix Representation Neale Swinnerton - @sw1nn
London Clojure User Group 11/2012 https://github.com/swannodette/enlive-tutorial Conclusion ML sits inside larger systems. Why Clojure for ML? It's not just about the theory
Maintainability It's not just about optimizing the maths
Part of a larger system
Connect with the real world
General purpose vs mathematical language What is Machine Learning? Machine Learning involves developing systems to process (potentially very large) datasets, developing algorithms and models that can then be used to make predictions of future events. Display Results Get Data
Profit? Access to Java ecosystem. Options to break out to high performance native code. Idiomatic clojure can perform 'quite' well in many circumstances. Clojure is *great* for the 'general' Scalability, Reliability, Maintainability important metrics for success.