PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive ”Backronym”.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Geramifard, Alborz; Dann, Christoph; Klein, Robert H.; Dabney, William; How, Jonathan P.: RLPy: a value-function-based reinforcement learning framework for education and research (2015) ioport
- Weninger, Felix: Introducing CURRENNT: the Munich open-source CUDA recurrent neural network toolkit (2015)
- Demšar, Janez; Curk, Tomaž; Erjavec, Aleš; Gorup, Črt; Hočevar, Tomaž; Milutinovič, Mitar; Možina, Martin; Polajnar, Matija; Toplak, Marko; Starič, Anže; Štajdohar, Miha; Umek, Lan; Žagar, Lan; Žbontar, Jure; Žitnik, Marinka; Zupan, Blaž: Orange: data mining toolbox in Python (2013)
- Ly, Daniel L.; Lipson, Hod: Learning symbolic representations of hybrid dynamical systems (2012)
- Kovacs, Tim; Egginton, Robert: On the analysis and design of software for reinforcement learning, with a survey of existing systems (2011) ioport
- Kumerički, Krešimir; Müller, Dieter; Schäfer, Andreas: Neural network generated parametrizations of deeply virtual Compton form factors (2011)
- Schaul, Tom; Bayer, Justin; Wierstra, Daan; Sun, Yi; Felder, Martin; Sehnke, Frank; Rückstieß, Thomas; Schmidhuber, Jürgen: PyBrain (2010) ioport
Further publications can be found at: http://pybrain.org/pages/publications