TinyRNN is a lightweight header-only C++11 library for building deep recurrent neural networks of any architecture from multilayered perceptrons to LSTM-like RNN's.

Upd. 11.03.2017: TinyRNN is a self-education project and is no longer being maintained.

TinyRNN is heavily influenced by synaptic.js, a javascript library by Juan Cazala based on the paper by Derek D. Monner and James A. Reggia -- "A generalized LSTM-like training algorithm for second-order recurrent neural networks".

Copyright © 2015 Peter Rudenko (peter.rudenko@gmail.com)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.