An aside on machine learning, and open-source contribution

So having forced my brain to code a vectorized cost function in Octave starting from the equation - a task that truly taxed skills that had lain dusty for decades, and involved a brief discussion with my private theoretical physicist - I've started to think maybe I might be capable of learning a new trick or two. This Stanford class just barely scratches the surface, of course, and my mathematical background is essentially nil, so I've got a steep hill to climb.

But. There are open-source machine learning projects out there. Perhaps it might be best to start contributing. So on that note: the mloss.org project database. 334 projects and counting.

And one of the things that caught my eye this week on the software development front is PVS-Studio, a static C/C++ code analyzer that finds common coding errors. There was briefly an article on it listing 91 such errors, but it was deleted. Of course, it would be a hell of a lot more interesting to have an open-source equivalent. If there isn't one, I intend to damn well start one, with a curated set of flags (this may be why the article disappeared, of course...).

Update after reading this: OK, so I'm an idiot. Sometimes it's easy to forget the last twenty years and the Internet and all. [also]

Anyway, the whole concept of static code analysis fits well with my vague idea of a "code understander" set loose on open-source code.