The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox, released under the LGPL, for rapidly building, training and evaluating modular learning architectures on large datasets. It builds functionality on top of the Modular toolkit for Data Processing (MDP). Using MDP, Oger provides: - Easily building, training and using modular structures of learning algorithms - A wide variety of state-of-the-art machine learning methods, such as PCA, ICA, SFA, RBMs, ...
The Oger toolbox builds functionality on top of MDP, such as: - Cross-validation of datasets - Grid-searching large parameter spaces - Processing of temporal datasets - Gradient-based training of deep learning architectures - Interface to the Speech Processing, Recognition, and Automatic Annotation Kit (SPRAAK)
In addition, several additional MDP nodes are provided by Oger, such as a: - Reservoir node - Leaky reservoir node - Ridge regression node - Conditional Restricted Boltzmann Machine (CRBM) node - Perceptron node
In particular, Oger is suitable for building architectures incorporating the so-called Reservoir Computing framework. Reservoir Computing is a learning framework (Verstraeten et al., 2007) whereby a random non-linear dynamical system (usually a recurrent neural network) is left untrained and used as input to a simple learning algorithm such as linear regression. A comprehensive review is available in (Lukosevicius and Jaeger, 2009).
For more info, please consult the online documentation, available at http://organic.elis.ugent.be/oger.
- Changes to previous version:
Initial Announcement on mloss.org.
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