recurrent_neural_network
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recurrent_neural_network [2019/04/11 16:35] burkov |
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| ===== Recommended Reading ===== | ===== Recommended Reading ===== | ||
| + | * [[https://www.dropbox.com/s/ouj8ddydc77tewo/ExtendedChapter6.pdf?dl=0|An extended version of Chapter 6 with RNN unfolding and bidirectional RNN]] | ||
| * [[http://karpathy.github.io/2015/05/21/rnn-effectiveness/|The Unreasonable Effectiveness of Recurrent Neural Networks]] by Andrej Karpathy (2015) | * [[http://karpathy.github.io/2015/05/21/rnn-effectiveness/|The Unreasonable Effectiveness of Recurrent Neural Networks]] by Andrej Karpathy (2015) | ||
| * [[https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5|Recurrent Neural Networks and LSTM]] by Niklas Donges (2018) | * [[https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5|Recurrent Neural Networks and LSTM]] by Niklas Donges (2018) | ||
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| * [[http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/|Implementing a GRU/LSTM RNN with Python and Theano]] by Denny Britz (2015) | * [[http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/|Implementing a GRU/LSTM RNN with Python and Theano]] by Denny Britz (2015) | ||
| * [[https://arxiv.org/abs/1701.03452|Simplified Minimal Gated Unit Variations for Recurrent Neural Networks]] by Joel Heck and Fathi Salem (2017) | * [[https://arxiv.org/abs/1701.03452|Simplified Minimal Gated Unit Variations for Recurrent Neural Networks]] by Joel Heck and Fathi Salem (2017) | ||
| + | * [[https://arxiv.org/abs/1706.03762|Attention Is All You Need]] by Vaswani et al. (2017), a state-of-the-art sequence-to-sequence model, plus an [[http://jalammar.github.io/illustrated-transformer/|illustrated guide]] | ||
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