Recurrent neural networks (RNNs) are a type of neural network designed to handle sequential or time-series data. Unlike feedforward neural networks, RNNs have a “memory” that allows them to process sequential inputs and maintain information about previous inputs.
The key feature of RNNs is their ability to feed their own outputs back into the network as inputs, allowing them to remember important information from previous inputs. This feedback loop creates a kind of “hidden state” that can influence the outputs of the network at each time step.
RNNs are particularly well-suited for tasks such as speech recognition, natural language processing, and time-series prediction. For example, an RNN can be used to predict the next word in a sentence based on the previous words, or to predict future stock prices based on past price data.
One limitation of RNNs is that they can suffer from the “vanishing gradient” problem, where the gradients used to update the weights of the network during training become very small or even zero. This can make it difficult for the network to learn long-term dependencies in the data.
To address this problem, several variations of RNNs have been developed, including long short-term memory (LSTM) networks and gated recurrent units (GRUs). These networks use specialized structures to better handle long-term dependencies in the data and avoid the vanishing gradient problem.
Overall, recurrent neural networks are a powerful tool for processing sequential data and making predictions based on time-series data. They are widely used in many applications, including speech recognition, natural language processing, and finance.