Recurrent Neural Network (RNN)

A recurrent neural network (RNN) is a type of artificial neural network that is designed to process sequential data by incorporating feedback loops, allowing information to persist and be shared across different time steps.RNNs are specifically designed to handle sequential data, such as time series or natural language, where the order of the input matters. Unlike traditional feedforward neural networks that process inputs independently, RNNs have connections that form a loop, enabling the network to retain information from previous steps and use it to influence the current step. This feedback mechanism allows RNNs to capture temporal dependencies and learn patterns over time, making them suitable for tasks like language translation, speech recognition, sentiment analysis, and predicting future values based on historical data. By incorporating memory into the network's architecture, RNNs can effectively model and process sequences of data.

In summary, an RNN is an artificial neural network designed to handle sequential data by incorporating feedback loops, enabling the network to retain and utilize information from previous steps, making it well-suited for tasks involving time series or natural language processing.