Recurrent Neural Networks (RNNs) are a class of neural networks specifically designed to process sequential data. Unlike traditional feedforward networks, RNNs have connections that loop back on themselves, allowing them to maintain a form of memory. This unique architecture enables RNNs to capture temporal dependencies in data, making them particularly effective for tasks such as language modeling, speech recognition, and time series prediction. By processing input sequences one element at a time while retaining information from previous steps, RNNs can understand context and relationships within the data, which is crucial for many applications in natural language processing and beyond.