In this project, we will attempt to build a character-level language model using an RNN to generate prose given some initial seed characters. The main task of a character-level language model is to predict the next character given all previous characters in a sequence of data. In other words, the function of an RNN is to generate text character by character.
To start with, we feed the RNN a huge chunk of text as input and ask it to model the probability distribution of the next character in the sequence, given a sequence of previous characters. These probability distributions conceived by the RNN model will then allow us to generate new text, one character at a time.
The first requirement for building a language model is to secure a corpus of text that the model can use to compute the probability distribution of various characters. The larger the input text corpus, the better the RNN will model the probabilities.
We do not have to strive a lot...