Recurrent Neural Networks, or RNNs, are 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 work by taking an input sequence one element at a time, updating their internal state with each new input. This ability to remember past information enables RNNs to capture patterns and dependencies in sequential data, making them effective for applications like language translation and sentiment analysis.