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recurrent_neural_network [2019/05/04 22:25]
burkov
recurrent_neural_network [2022/06/15 02:27] (current)
burkov [Recommended Reading]
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 ===== Recommended Reading ===== ===== Recommended Reading =====
  
-  * [[https://​www.dropbox.com/​s/​nprpvhgjrj7tao1/​ExtendedChapter6.pdf?​dl=0|An extended version of Chapter 6 with RNN unfolding and bidirectional RNN]]+  * [[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]] plus an [[http://​nlp.seas.harvard.edu/​annotated-transformer/​|annotated paper with code]].