Teknosys learning library

RNN, LSTM, and GRU: learning sequences

Recurrent neural networks (RNNs) process sequences step by step, carrying a hidden state between tokens. Long short-term memory (LSTM) networks and gated recurrent units (GRUs) introduce gates that help manage memory and reduce vanishing-gradient problems. They have been used for language modeling, sequence labeling, and translation. Transformers allow more parallel processing during training, but recurrent architectures remain useful for understanding sequence modeling and constrained applications.

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