Note the GPL license. Nothing wrong with that but if you copy one of these into a code base you're developing, GPL applies to the whole "derived work". If you're sufficiently cautious about legal matters it may be be best to avoid looking at this code at all if you plan to implement any of these techniques in a non-GPL project.
Is that based on a common book on numerical analysis?
Because one thing I'd miss in the examples would be a few lines of comments - if they're meant to be illustrative of a book that's ok, but learning the other way (looking at source code, structure and comments) is also a good way for many people to understand. If you're well read in source code, you can read it to understand the math better.
In terms of education this makes sense. You can read the code of common numerical algorithms, written for clarity rather than speed, and learn how they works. Not very comprehensive yet, but it can get there. Also, contributing to this repo is a good way to learn.
That said, for actually getting the job done, you would probably use a library like numpy instead of a library like this, as it is more powerful, both in terms of execution speed and speed of development.
This has to be one of the easiest open source projects you can contribute to if you are looking to do so.
Each contribution is small and modular, so you don't have to learn several thousand lines of code.
The "real world" knowledge you have to learn is pretty specific and easy to convert into code (compared to, say, legal opinions or object recognition).
But that is for good reason -- it seems to exist mainly for pedagogical reasons. It is more like contributing to a textbook. Contributing to the "real world" software (NumPY, OpenBLAS, etc) which is optimized and battle-hardened is tougher exactly for that reason.
