Hence, a new architecture is required for handling data that arrives sequentially, and where both or either of its input values and output values are of variable length for example, the words in a sentence in a language translation application. In this case, both the input and output to the model are of varying lengths as in the fourth mode previously. Also, in order to predict subsequent words given the current word, previous words need to be known as well. This new neural network architecture is called an RNN, and it is specifically designed to handle sequential data.
The term recurrent arises because such models perform the same computation on every element of a sequence, where each output is dependent on previous output. Theoretically, each output depends on all of the previous output items, but in practical terms, RNNs are limited to looking back just...
