Recurrent Neural Networks
- Acknowledgement
https://d2l.ai/chapter%5Frecurrent-neural-networks/index.html
https://github.com/fastai/fastbook/blob/master/12%5Fnlp%5Fdive.ipynb
https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
https://colah.github.io/posts/2015-08-Understanding-LSTMs/ [Mainly LSTM]
https://deeplearning.cs.cmu.edu/S20/document/recitation/recitation-7.pdf
http://cs231n.stanford.edu/slides/2017/cs231n%5F2017%5Flecture10.pdf
https://github.com/fastai/fastbook/blob/master/12%5Fnlp%5Fdive.ipynb [ fast ai nlp dive rnn archi]
http://ethen8181.github.io/machine-learning/deep%5Flearning/rnn/1%5Fpytorch%5Frnn.html#Recurrent-Neural-Network-(RNN) [Main reference]
https://medium.com/ecovisioneth/building-deep-multi-layer-recurrent-neural-networks-with-star-cell-2f01acdb73a7 [Multi Layer]
https://towardsdatascience.com/pytorch-basics-how-to-train-your-neural-net-intro-to-rnn-cb6ebc594677
https://www.jeremyjordan.me/introduction-to-recurrent-neural-networks/
https://towardsdatascience.com/pytorch-basics-how-to-train-your-neural-net-intro-to-rnn-cb6ebc594677
Sebastian Raschka Character Generation using lstm cell pytorch
Recurrent neural networks (RNNs) are designed to better handle sequential information. (text and stocks)
RNNs introduce state variables(hidden state) to store past information, together with the current inputs, to determine the current outputs.
Figure 1: rnn vs nn
Figure 2: a folded rnn
Figure 3: a unfolded rnn
- Internal State of RNN
Figure 4: a state diagram,