So far, we have seen the ANNs where the input signals are propagated to the output layer in the forward pass and the weights are optimized in a recursive manner in order to train the model for generalizing the new input data based on the training set provided as input.
A special case real-life problem is optimizing the ANN for training sequences of data, for example, text, speech, or any other form of audio input. In simple terms, when the output of one forward propagation is fed as input for the next iteration of training, the network topology is called a recurrent neural network (RNN).
