Recurrent neural networks (RNNs) are a family of neural networks for processing sequential data. RNNs are generally used to implement language models. We, as humans, base much of our language understanding on the context. For example, let's consider the sentence Christmas falls in the month of --------. It is easy to fill in the blank with the word December. The essential idea here is that there is information about the last word encoded in the previous elements of the sentence.
The central theme behind the RNN architecture is to exploit the sequential structure of the data. As the name suggests, RNNs operate in a recurrent way. Essentially, this means that the same operation is performed for every element of a sequence or sentence, with its output depending on the current input and the previous operations.
An RNN works by looping an output...
