How do gated recurrent units (GRUs) enhance the processing of real-time data in recurrent neural networks (RNNs)?

Gated recurrent units, or GRUs, improve real‑time data handling in RNNs by using two gates that decide what information to keep and what to forget as each new input arrives. The update gate controls how much of the past hidden state is carried forward, while the reset gate decides how much of the old information should be discarded before combining it with the new input. This gating mechanism lets the network quickly adapt to changes in the input stream without the long‑term memory problem that standard RNNs suffer from, reducing the chance of “vanishing gradients. ” For example, when processing a live speech signal, a GRU can instantly ignore irrelevant background noise while preserving the important spoken words, allowing the model to respond accurately to each new frame of audio. By balancing memory and flexibility, GRUs enable RNNs to learn from and react to rapidly changing real‑time data more efficiently.