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==== Generic ML Libraries ==== *[http://www.cs.waikato.ac.nz/ml/weka/ Weka] **a collection of data mining tools and machine learning algorithms. *[http://scikit-learn.sourceforge.net/ scikits.learn] **Machine learning Python package *[http://pypi.python.org/pypi/scikits.statsmodels scikits.statsmodels] **Statistical models to go with scipy *[http://pybrain.org PyBrain] **Does feedforward, recurrent, SOM, deep belief nets. *[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM] **c-based SVM package *[http://pyml.sourceforge.net PyML] *[http://mdp-toolkit.sourceforge.net/ MDP] **Modular framework, has lots of stuff! *[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here *[http://sympy.org sympy] Does symbolic math *[http://waffles.sourceforge.net/ Waffles] **Open source C++ set of machine learning command line tools. *[http://rapid-i.com/content/view/181/196/ RapidMiner] *[http://www.mrpt.org/ Mobile Robotic Programming Toolkit] *[http://nipy.sourceforge.net/nitime/ nitime] **NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting. *[http://pandas.pydata.org/ Pandas] **Data analysis workflow in python *[http://www.pytables.org/moin PyTables] **Adds querying capabilities to HDF5 files *[http://statsmodels.sourceforge.net/ statsmodels] **Regression, time series analysis, statistics stuff for python *[https://github.com/JohnLangford/vowpal_wabbit/wiki Vowpal Wabbit] **"Intrinsically Fast" implementation of gradient descent for large datasets *[http://www.shogun-toolbox.org/ Shogun] **Fast implementations of SVMs *[http://www.mlpack.org/ MLPACK] **High performance scalable ML Library *[http://www.torch.ch/ Torch] **MATLAB-like environment for state-of-the art ML libraries written in LUA
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