[krull10]

General scientific computing is pretty good across the Julia ecosystem, from optimization, to ODE and now PDE solver libraries, to various statistics and inference packages, etc. It lacks the deep NN tooling or breadth of ML libraries of Python, and nothing matches R for breadth of stats libraries, but for most other scientific computing it is really great at this point.

[stn8188]

I am by no means an expert, but I used Flux.jl for a convolutional neural net in electromagnetics for my latest paper and it was such a breath of fresh air compared to Python and PyTorch. (I'm an EE and not great at programming, so I found a lot of frustration in PyTorch). Even though the Keras library in Python is pretty nice, even then I got myself into some odd pickles when trying to do some custom layers which used FFT processing as it relates to gradient computation. Things are much smoother in Julia, and that doesn't even count how much easier the Plots library is! I'm ashamed to admit that I have no idea how to manipulate the figures and axes in Matplotlib without extensive googling.

[kedarkhand]

I am really waiting for the day Julia ML ecosystem improves and I can jump ship to it.