An RNN is a neural network that can process sequential data. In a regular feed-forward neural network, the current input is considered for neurons in the next layer. On the other hand, an RNN can accept previously received inputs as well. It can also use memory to memorize previous inputs. So, it is capable of preserving long-term dependencies throughout the training session. RNN is a popular choice for NLP tasks such as speech recognition. In practice, a slightly variant structure called Long Short-Term Memory (LSTM) is used as a better alternative to RNN.