Machine and Deep Learning Using MATLAB

9 Recurrent Neural Network (RNN)

A Recurrent Neural Network (RNN) is a type of artificial neural network designed to process sequential data by maintaining an internal memory or state. Unlike traditional feedforward neural networks, which process data in a single pass, RNNs can take into account the previous steps in the sequence, making them well-suited for tasks that involve sequential or time-dependent data.

The key feature of an RNN is its ability to use its internal memory to process sequences of inputs of varying lengths. It achieves this by applying the same set of weights to each element of the sequence while maintaining a hidden state that represents the context from previous inputs. This hidden state is updated at each step, allowing the network to capture the dependencies and patterns in the sequence.

The basic building block of an RNN is a recurrent neuron, which takes an input along with the previous hidden state and produces an output and a new hidden state. The output can be used for prediction or further processing, and the new hidden state becomes the input for the next step in the sequence. This recursive structure enables the RNN to pass information from one step to another, allowing it to model long-term dependencies.

RNNs have been successfully applied to various tasks involving sequential data, such as natural language processing, speech recognition, machine translation, sentiment analysis, and time series forecasting. However, traditional RNNs can struggle ...