They are indeed concise and beautiful, but some of the implementations I checked are quite naïve. They're great for academic purposes and for some simple problems they will work just fine, but I would advise against using them for real-world problems.
For instance, for some of the regression algorithms the pseudo-inverse of the matrix is used to solve the normal equations. Again, for small, well-behaved problems it is OK, but for larger scale, less well-behaved problems, it is actually dangerous (numerical instabilities and other issues). There are better, faster, more stable approaches. (PhD in Applied Math here)
BTW, I have been using Julia for work for several years now and I think it is a fantastic language for scientific computing. For those who use Matlab/Numpy regularly, you should definitely check it out.
For instance, for some of the regression algorithms the pseudo-inverse of the matrix is used to solve the normal equations. Again, for small, well-behaved problems it is OK, but for larger scale, less well-behaved problems, it is actually dangerous (numerical instabilities and other issues). There are better, faster, more stable approaches. (PhD in Applied Math here)
BTW, I have been using Julia for work for several years now and I think it is a fantastic language for scientific computing. For those who use Matlab/Numpy regularly, you should definitely check it out.