Tagged
LSTM
NLP Part 3: RNN and Sequence Modeling
How RNNs, LSTMs, and GRUs process sequences with memory. We derive vanishing gradients from first principles, build a character-level text generator, and implement a Seq2Seq translator in PyTorch.
Time Series Forecasting (2): LSTM -- Gate Mechanisms and Long-Term Dependencies
How LSTM's forget, input, and output gates solve the vanishing gradient problem. Complete PyTorch code for time series forecasting with practical tuning tips.