Recurrent Neural Networks (RNNs) are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows them to exhibit temporal dynamic behavior for a time sequence, making them ideal for tasks that involve sequential data. Unlike traditional neural networks, RNNs can use their internal state (memory) to process sequences of inputs, making them suitable for applications like speech recognition, time series prediction, and natural language processing.
