For anything involving differential equations, Julia is way better than anything else. I also wouldn't be at all surprised if Julia catches up to python for way more machine learning within the next year
Agree to that completely. It is also hard for the libraries to spend their energy / time in building for languages that covers only a few percentage of total users. :(
There is a rather active community for Go in data science and machine learning. +1700 members on the #data-science channel on the Gopher slack, a few books on the topic, and a few libraries:
Can you say a bit about the advantages of each? I'm just starting to learn deep learning and planning to use it for NLP. I'm taking the python-based fast.ai course. But I'm tempted by the elegance of Julia.
If you're starting you should use Python and when you have a more solid grasp you can switch to Julia. That's mostly due to the fact that there are much more tutorials and resources for dl in python.
Is anyone still excited about differentiable programming? At least in NLP it seems like a lot of the energy has shifted to large scale overparameterized models like BERT, e.g. you don't need arbitrary control flow in your model, all you need is attention.
The differentiable programming framing is quite useful in the scientific simulation space, because it lets you couple traditional physics simulations to either neural surrogates or some sort of bayesian model. Enough of it is happening in the Julia world that were very actively improving the compiler to support it.