Gated Recurrent Unit (GRU)

A Gated Recurrent Unit (GRU) is a type of recurrent neural network (RNN) architecture. It's designed to handle the vanishing gradient problem often encountered when training standard RNNs, especially with long sequences of data. GRUs use "gates" to control the flow of information, deciding what information to keep and what to discard at each time step. These gates are learned during training and allow the network to selectively remember or forget previous states, making it more effective at capturing long-range dependencies in sequential data.

Visit the following resources to learn more:

• @article@Understanding Gated Recurrent Unit (GRU) in Deep Learning
• @article@GRU Recurrent Neural Networks – A Smart Way to Predict Sequences in Python
• @video@Simple Explanation of GRU (Gated Recurrent Units)