What is a recurrent neural network (RNN) and how does it handle sequential data?

A recurrent neural network (RNN) is a type of neural network designed for processing sequential data by maintaining a hidden state that captures information about previous inputs in the sequence. Unlike traditional feedforward networks, RNNs have connections that allow data to flow in loops, enabling them to remember past information. This memory capability is particularly useful in tasks such as language modeling and speech recognition, where the order of data points affects the output. However, RNNs can struggle with long-term dependencies due to vanishing gradients, which led to the development of more advanced architectures like LSTMs and GRUs.