A Gated Recurrent Unit (GRU) is a type of neural network architecture used in machine learning, particularly for processing sequences of data, like time series or natural language. It helps the model remember important information over time while forgetting less relevant details. This is achieved through its unique gating mechanisms, which control the flow of information. GRUs are similar to another architecture called Long Short-Term Memory (LSTM) networks but are simpler and often faster to train. They are widely used in applications such as speech recognition, language translation, and even in generating text, making them a valuable tool in the field of artificial intelligence.