Dear NumPy and SciPy users, we are proud to announce release 2.3 of the Modular toolkit for Data Processing (MDP): a Python data processing framework. The base of readily available algorithms includes Principal Component Analysis (PCA and NIPALS), four flavors of Independent Component Analysis (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Independent Slow Feature Analysis, Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant Analysis, Factor Analysis, Restricted Boltzmann Machine, and many more. What's new in version 2.3? -------------------------- - Enhanced PCA nodes (with SVD, automatic dimensionality reduction, and iterative algorithms). - A complete implementation of the FastICA algorithm. - JADE and TDSEP nodes for more fun with ICA. - Restricted Boltzmann Machine nodes. - The new subpackage "hinet" allows combining nodes in arbitrary feed-forward network architectures with a HTML visualization tool. - The tutorial has been updated with a section on hierarchical networks. - MDP integrated into the official Debian repository as "python-mdp". - A bunch of bug-fixes. Resources --------- Download: http://sourceforge.net/project/showfiles.php?group_id=116959 Homepage: http://mdp-toolkit.sourceforge.net Mailing list: http://sourceforge.net/mail/?group_id=116959 -- Pietro Berkes Gatsby Computational Neuroscience Unit UCL London, United Kingdom Niko Wilbert Institute for Theoretical Biology Humboldt-University Berlin, Germany Tiziano Zito Bernstein Center for Computational Neuroscience Humboldt-University Berlin, Germany
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Tiziano Zito