RNNs are one of several different neural network architectures.
Recurrent neural network vs. feed-forward neural network
Like RNNs, feed-forward neural networks are artificial neural networks that pass information from one end to the other end of the architecture. A feed-forward neural network can perform simple classification, regression, or recognition tasks, but it can’t remember the previous input that it has processed. For example, it forgets Apple by the time its neuron processes the word is. The RNN overcomes this memory limitation by including a hidden memory state in the neuron.
Recurrent neural network vs. convolutional neural networks
Convolutional neural networks are artificial neural networks that are designed to process spatial data. You can use convolutional neural networks to extract spatial information from videos and images by passing them through a series of convolutional and pooling layers in the neural network. RNNs are designed to capture long-term dependencies in sequential data