Recurrent Neural Network

A recurrent neural network (RNN) is a type of artificial neural network designed to process sequential data by maintaining a memory of previous inputs. Unlike feedforward networks, RNNs use loops that pass information from one time step to the next, enabling them to capture context and order. During training, each element in a sequence – such as words in a sentence or measurements in a time series – updates the network’s hidden state, which influences future outputs. This makes RNNs well-suited for tasks like language modeling, speech recognition, machine translation and time-series forecasting. By learning temporal dependencies, RNNs can predict or generate sequences that reflect underlying patterns.