Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed for processing sequential data. Unlike traditional feedforward networks, RNNs have connections that loop back on themselves, allowing them to maintain a memory of previous inputs. This characteristic makes them particularly effective for tasks such as time series prediction, natural language processing, and speech recognition, where context and order are crucial. Common use cases include language modeling, where RNNs predict the next word in a sequence, and sentiment analysis, where they help understand the emotional tone of text. RNNs can be further enhanced with architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) to address issues like vanishing gradients.