A recurrent neural network (RNN) is a type of artificial neural network that is designed to process sequential data or data with temporal dependencies. Unlike feedforward neural networks, which process data in a single forward pass without any memory of previous inputs, RNNs have a memory component that allows them to retain information about past inputs and use it to make predictions or generate outputs. The key feature of an RNN is its ability to maintain an internal state or hidden state, which captures the context or information from previous inputs. This hidden state is updated at each time step based on the current input and the previous hidden state. It serves as a form of memory that allows the network to learn long-term dependencies in sequential data. RNNs are commonly used in various natural language processing tasks such as language modeling, machine translation, speech recognition, and sentiment analysis. They can also be applied to other types of sequential data like time series analysis, video processing, and music generation. One popular variant of RNN is the Long Short-Term Memory (LSTM) network, which addresses some issues related to vanishing/exploding gradients that can occur with standard RNNs. LSTMs have additional gates that control the flow of information within the network, allowing them to selectively remember or forget certain information. Another variant is the Gated Recurrent Unit (GRU), which has similar gating mechanisms as LSTM but with fewer parameters. GRUs are computationally less expensive than LSTMs but can still capture long-term dependencies effectively. Overall, recurrent neural networks are powerful models for processing sequential data due to their ability to capture temporal dependencies and contextual information from past inputs. They have significantly contributed to advancements in various fields involving sequential data analysis and generation. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Recurrent Neural Network") or contains(subject, "RNN") ```