Oger: modular learning architectures for large-scale sequential processing. Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large data sets. It builds on MDP for its modularity, and adds processing of sequential data sets, gradient descent training, several cross-validation schemes and parallel parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, conditional restricted Boltzmann machine (CRBM) and others, including GPU accelerated versions. Oger is released under the GNU LGPL, and is available from url{http://organic.elis.ugent.be/oger}.

References in zbMATH (referenced in 2 articles )

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  1. Ibáñez-Soria, D.; Garcia-Ojalvo, J.; Soria-Frisch, A.; Ruffini, G.: Detection of generalized synchronization using echo state networks (2018)
  2. Verstraeten, David; Schrauwen, Benjamin; Dieleman, Sander; Brakel, Philemon; Buteneers, Pieter; Pecevski, Dejan: Oger: modular learning architectures for large-scale sequential processing (2012) ioport

Further publications can be found at: http://organic.elis.ugent.be/publications?sort=year&order=desc