What is Gated Recurrent Unit (GRU)?
A gated recurrent unit (GRU) is part of a specific model of a recurrent neural network that wants to use connections across a sequence of nodes to perform machine learning tasks related to memory and clustering, such as speech recognition. Gated Recurrent Units help to adjust the input weights of the neural networks in order to solve the problem of the vanishing gradient that often occurs with recurring neural networks.
As a refinement of the general recurrent neural network structure, keyed recurrent units have what is known as an update gate and a reset gate. Using these two vectors, the model refines outputs by controlling the flow of information through the model. Like other types of recurring network models, models with keyed recurring units can store information over a period of time.
Because of this, one of the easiest ways to describe this type of technology is that it is a 'memory-centric' neural network. In contrast, other types of neural networks without controlled repeating units often do not have the ability to retain information.
In addition to speech recognition, neural network models using keyed repeating units can be used for human genome exploration, handwriting analysis, and much more. Some of these innovative networks are used in stock market analysis and government work. Many of them use the simulated ability of machines to memorize information.