1. Scaling Machine Learning in Python


    from PyData / Added

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    In this talk we will introduce the typical predictive modeling tasks on "not-so-big-data-but-not- quite-small-either" that benefit from distributed the work on several cores or nodes in a small cluster (e.g. 20 * 8 cores). We will talk about cross validation, grid search, ensemble learning, model averaging, numpy memory mapping, Hadoop or Disco MapReduce, MPI AllReduce and disk & memory locality. We will also feature some quick demos using scikit-learn and IPython.parallel from the notebook on an spot-instance EC2 cluster managed by StarCluster.

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    • SciKit-Learn Tutorial - Jake VanderPlas


      from PyData / Added

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      Machine Learning is a discipline involving algorithms designed to find patterns in and make predictions about data. It is nearly ubiquitous in our world today, and used in everything from web searches to financial forecasts to studies of the nature of the Universe. This tutorial will offer an introduction to scikit-learn, a python machine learning package, and to the central concepts of Machine Learning. We will introduce the basic categories of learning problems and how to implement them using scikit-learn. From this foundation, we will explore practical examples of machine learning using real-world data, from handwriting analysis to automated classification of astronomical images. This talk was presented at PyData NYC 2012: http://nyc2012.pydata.org/. If you are interested in this topic, be sure to check out PyData Silicon Valley in March of 2013: http://sv2013.pydata.org/

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      • libsvm gui


        from Fabian Pedregosa / Added

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        Showcase of libsvm-gui, a demo included in scikit-learn. Music: "Baraja de cuchillos" by Joe Crepúsculo, album "Supercrepus". Source code can be found here: http://scikit-learn.sourceforge.net/0.6/auto_examples/applications/svm_gui.html

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