What Are GRU Networks and How Do They Fix RNN Memory Problems

Recurrent Neural Networks (RNNs) are designed to process sequential data — such as text or time-series numbers — by passing a hidden state forward through each time step, acting as a running memory. However, standard RNNs struggle with long sequences because gradients shrink during training, making it hard for the network to learn connections between distant elements. To address this, researchers developed the Gated Recurrent Unit (GRU), a type of RNN that uses internal mechanisms called gates to control what information is retained or discarded at each step. A GRU cell relies on two gates — the update gate and the reset gate — which together determine how much past memory to carry forward versus how much new input to incorporate. This gating mechanism allows GRUs to preserve relevant context over longer sequences, making them significantly more effective for tasks like language translation, sentiment analysis, and time-series prediction.