Latent Dirichlet Allocation (LDA)

If we assume documents are comprised of different topics, then we can assume that certain words belong to certain topics. Therefore, a document is a set of words that are more or less grouped according to their topic. For example: A sports story can have a few topics in it such as "the actual game" (score, plays, etc.), "the players" (injuries, drug testing, etc.) and "speculation" (what will happen against the next rival). For each of these topics there are words that are strongly associated with them. For example: "score" is strongly associated to "the actual game" topic, while "steroids" is probably associated to "the players" topic. However, "score" may also be associated with the "speculation" topic albeit less strongly.

LDA attempts to group these words automatically from large collections fo text. Github Repo.

Other Simple Scripts

I have written a bunch of simpler scripts to emulate argparse in Java, small unit testing software for grading java assignments, models to convert simple beamer (latex presentations) into Reveal.js, a student appointment system, etc. these are my pet projects.

Check my "pets" repo. on Github

You are welcome to check other larger projects on my Github repos.