Recurrent Neural Network (RNN) is a type of artificial neural network where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior, making it suitable for tasks that involve sequential data.
In neural networks, RNN refers to a class of models designed to recognize patterns in sequences of data, such as time series, speech, or text. RNNs can process inputs of varying lengths by maintaining a hidden state that captures information from previous steps in the sequence.
RNN stands for Recurrent Neural Network. It emphasizes the network’s ability to handle sequences by “recurrent” connections that loop back in the network.
RNNs are applied in various fields such as language modeling, machine translation, speech recognition, and time series prediction. They are particularly useful for tasks where the context of previous inputs influences the current output.
The basic RNN network consists of a layer of neurons where the output from each neuron at each time step is fed back into the network along with the next input. This feedback loop allows the network to maintain a hidden state that carries information from previous inputs, enabling it to process sequences of data.