contributing to mlpack

mlpack is an open-source project, so anyone is welcome to become a part of the community and contribute. There is no need to be a machine learning expert to participate—often, there are many tasks to be done that don't require in-depth knowledge.

mlpack also participates in Google Summer of Code; for more information on that, see this page.

A good place to start is to download mlpack, compile it from source (tutorial), and set up a development environment. Once you've done this, it would probably be useful to get a feel for some of the algorithms mlpack implements by using some of the command-line programs (man pages) or Python bindings (documentation) to perform some machine learning tasks.

Next, you can implement some simple mlpack programs and read through the other tutorials, and by the time you've finished with that you should have a pretty good handle on the way the library works.

At this point, you're probably ready to jump in and start contributing. Development is done on Github, so you'll need an account there, and you can submit patches or contributions via pull requests. Below are some useful links and tips:

Still not sure, or have some questions? Get in touch via IRC, the mailing list, or any other way.