# pyrenn: A recurrent neural network toolbox for python and matlab¶

Maintainer: | Dennis Atabay, <dennis.atabay@tum.de> |
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Organization: | Institute for Energy Economy and Application Technology, Technische Universität München |

Version: | 0.1 |

Date: | Jun 30, 2018 |

Copyright: | This documentation is licensed under a Creative Commons Attribution 4.0 International license. |

## Contents¶

This documentation contains the following pages:

## Features¶

- pyrenn allows to create a wide range of (recurrent) neural network configurations
- It is very easy to create, train and use neural networks
- It uses the Levenberg–Marquardt algorithm (a second-order Quasi-Newton optimization method) for training, which is much faster than first-order methods like gradient descent. In the matlab version additionally the Broyden–Fletcher–Goldfarb–Shanno algorithm is implemented
- The python version is written in pure python and numpy and the matlab version in pure matlab (no toolboxes needed)
- Real-Time Recurrent Learning (RTRL) algorithm and Backpropagation Through Time (BPTT) algorithm are implemented and can be used to implement further training algorithms
- It comes with various examples which show how to create, train and use the neural network