Aiconomy

Recurrent Neural Network (RNN)

A neural network architecture designed for sequential data that maintains a hidden state across time steps, allowing it to process inputs of variable length and capture temporal patterns.

RNNs were the dominant architecture for sequence tasks like language modeling, speech recognition, and time-series prediction before transformers. They process data one step at a time, maintaining a memory of previous inputs through their hidden state. Variants like LSTM and GRU addressed the vanishing gradient problem that limited basic RNNs. While transformers have largely replaced RNNs for NLP tasks, recurrent architectures remain competitive for streaming applications and are experiencing a revival through state-space models like Mamba.

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