Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user’s perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer’s perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

References in zbMATH (referenced in 9 articles )

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  1. Escalante-B., Alberto N.; Wiskott, Laurenz: Improved graph-based SFA: information preservation complements the slowness principle (2020)
  2. Fabian Sinz; Jörn-Philipp Lies; Sebastian Gerwinn; Matthias Bethge: Natter: A Python Natural Image Statistics Toolbox (2014) not zbMATH
  3. Sprekeler, Henning; Zito, Tiziano; Wiskott, Laurenz: An extension of slow feature analysis for nonlinear blind source separation (2014)
  4. Hunt, Jonathan J.; Ibbotson, Michael; Goodhill, Geoffrey J.: Sparse coding on the spot: spontaneous retinal waves suffice for orientation selectivity (2012)
  5. Kompella, Varun Raj; Luciw, Matthew; Schmidhuber, Jürgen: Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams (2012)
  6. Verstraeten, David; Schrauwen, Benjamin; Dieleman, Sander; Brakel, Philemon; Buteneers, Pieter; Pecevski, Dejan: Oger: modular learning architectures for large-scale sequential processing (2012) ioport
  7. Kovacs, Tim; Egginton, Robert: On the analysis and design of software for reinforcement learning, with a survey of existing systems (2011) ioport
  8. Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu; Duchesnay, Édouard: Scikit-learn: machine learning in Python (2011)
  9. Klampfl, Stefan; Maass, Wolfgang: A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction (2010)