Recurrent neural networks (RNNs) are a type of artificial neural network designed to handle sequential data and time series. Unlike traditional feedforward neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a memory of past inputs. This enables RNNs to effectively model and predict sequences of data, making them well-suited for tasks like language modeling, speech recognition, and time series analysis. RNNs have the ability to learn patterns and dependencies in sequential data, making them useful for tasks that require understanding context and temporal relationships. However, they can suffer from issues like vanishing gradients and difficulty in learning long-term dependencies. Various modifications and improvements have been made to RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, to address these challenges and improve their performance in handling sequential data.