Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture that's particularly effective for processing sequential data like text, speech, and time-series data. They were introduced as a simpler, more computationally efficient alternative to Long Short-Term Memory (LSTM) networks, while still addressing the vanishing gradient problem that plagues traditional RNNs.

How GRUs Work

The core of a GRU's functionality lies in its two "gates," which are special mechanisms that regulate the flow of information. These gates are:

• Reset Gate: This gate determines how much of the past information (from the previous hidden state) to forget. It helps the network to discard irrelevant information and focus on the current input. If the reset gate's value is close to 0, it essentially erases the previous state, allowing the network to start fresh.
• Update Gate: This gate controls how much of the past information should be carried forward to the new hidden state and how much of the new candidate information should be incorporated. It helps the network to decide what's important from the past and what's important from the current input.