Gated Recurrent Units (GRU)

Gated Recurrent Units (GRUs) are a type of neural network architecture used in machine learning, particularly for processing sequences of data, like text or time series. GRUs help the model remember important information from previous data while forgetting less relevant details. They achieve this through mechanisms called gates, which control what information to keep or discard. This design makes GRUs efficient and effective for tasks such as language translation or speech recognition, where understanding context over time is essential. Essentially, they help computers learn from sequences more intelligently by managing memory and attention.