Gated Recurrent Unit (GRU)

A Gated Recurrent Unit (GRU) is a type of neural network designed to process sequences of data, like sentences or time series. It has a special mechanism called "gates" that decide what information to keep or forget as it moves through the sequence. This allows GRUs to understand context over long sequences without losing important details, making them efficient for tasks like language translation, speech recognition, or forecasting. Essentially, GRUs selectively remember relevant information and ignore less useful parts, enabling more effective analysis of sequential data.