A Recurrent Neural Network, or RNN, is a type of artificial neural network designed for processing sequences of data. Unlike traditional neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a memory of previous inputs. This makes them particularly useful for tasks involving time series data, natural language processing, and speech recognition. RNNs can learn patterns in sequential data by using their internal memory to remember information from earlier in the sequence. However, they can struggle with long-term dependencies due to issues like vanishing gradients. Variants like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) have been developed to address these challenges.