Additional resources are available here:
- Chen, G. (2016). A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint arXiv:1610.02583: https://arxiv.org/pdf/1610.02583.pdf
- Cho, K., Van Merriƫnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078: https://arxiv.org/pdf/1406.1078v3.pdf
- Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555: https://arxiv.org/pdf/1412.3555.pdf
- Hochreiter, S., and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780: http://www.bioinf.jku.at/publications/older/2604.pdf
- Pascanu, R., Mikolov, T., and Bengio, Y. (2013, February). On the difficulty of training recurrent neural networks. In International conference on machine learning (pp. 1310-1318): https://arxiv.org/pdf/1211.5063v2.pdf
- http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- http://colah.github.io/posts/2015-08-Understanding-LSTMs/