RNN

Recurrent Neural Networks (RNNs) process sequential data by maintaining a hidden state that acts as a memory of previous inputs.

RNNs excel at handling time-series data and natural language by looping information through hidden layers. Unlike feedforward networks, they use internal state (memory) to process sequences of varying lengths. This architecture is foundational for tasks like Google Translate's early iterations and stock market volatility modeling. While standard RNNs often struggle with the vanishing gradient problem, their derivatives like LSTMs and GRUs remain critical for real-time signal processing and sequential pattern recognition.

https://pytorch.org/docs/stable/generated/torch.nn.RNN.html