How does Julia compare to Python+Numpy+scipy and Octave?
Re:A new release for GNU Octave
by UncleOxidant on Wednesday December 16, 2020 @02:02PM
(#25446867)
It compares quite favorably. The nice thing is that most of Numpy and Octave functionality is already builtin to Julia (multidimensional matrices are first class as they are in Matlab/Octave). Julia performance is quite good and multiple dispatch is great for composing packages (once you get used to it).
Check out SciML, Julia's answer to scipy (https://sciml.ai/) and Flux Julia's answer to PyTorch/Tensorflow (https://fluxml.ai/). Are these projects as mature as their Python counterparts? To be honest, no they're not, but they are making pretty rapid progress.
I haven't tried it out. It seems really interesting, though. A guy in my group loved it, but he graduated. I haven't yet so I guess we can put at least one point on the board for Julia!
I've done tiny prototypes that I later turned into a nice little Python+Numpy+Scipy scripts, and the final Python version was much nicer, but writing it was still more like a couple hour project, compared to the real-time process of figuring out what I wanted, done in Octave.