RNN and LSTM || Part 02
RNN and LSTM: Part 02
In this session, I have covered an overview of the LSTM architecture and explained why LSTM is chosen over traditional RNNs. Additionally, I discussed the vanishing gradient problem and the exploding gradient problem.
Moreover, each LSTM unit has 3 gates:
Forget Gate
Input Gate
Update Gate
In a vanilla RNN, one activation function, tanh, is used, whereas in LSTM, two different activation functions (sigmoid and tanh) are utilized, and so on.
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