PyBrain

Tom Schaul; Justin Bayer; Daan Wierstra; Yi Sun; Martin Felder; Frank Sehnke; Thomas Rückstieß; Jürgen Schmidhuber

PyBrain

Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber

Research output: Contribution to journal › Article › peer-review

Abstract

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep belief networks. © 2010 Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß and Jürgen Schmidhuber.

Original language	English (US)
Pages (from-to)	743-746
Number of pages	4
Journal	Journal of Machine Learning Research
Volume	11
State	Published - Feb 1 2010
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Artificial Intelligence
Software
Statistics and Probability
Control and Systems Engineering

Cite this

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title = "PyBrain",

abstract = "PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep belief networks. {\textcopyright} 2010 Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas R{\"u}ckstie{\ss} and J{\"u}rgen Schmidhuber.",

author = "Tom Schaul and Justin Bayer and Daan Wierstra and Yi Sun and Martin Felder and Frank Sehnke and Thomas R{\"u}ckstie{\ss} and J{\"u}rgen Schmidhuber",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-14",

year = "2010",

month = feb,

day = "1",

language = "English (US)",

volume = "11",

pages = "743--746",

journal = "Journal of Machine Learning Research",

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AU - Schaul, Tom

AU - Bayer, Justin

AU - Wierstra, Daan

AU - Sun, Yi

AU - Felder, Martin

AU - Sehnke, Frank

AU - Rückstieß, Thomas

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2010/2/1

Y1 - 2010/2/1

N2 - PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep belief networks. © 2010 Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß and Jürgen Schmidhuber.

AB - PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep belief networks. © 2010 Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß and Jürgen Schmidhuber.

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M3 - Article

SN - 1532-4435

VL - 11

SP - 743

EP - 746

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

ER -

PyBrain

Abstract

Bibliographical note

ASJC Scopus subject areas

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Cite this