I do get where you are coming from. Indeed, it makes little sense to use Julia for lots of machine learning when PyTorch and Jax are just so good. And it sounds like you don't want to use Julia, so who am I to try and convince you? Python/R are capable languages.
But, there are still reasons I reach for Julia.
Interesting packages where I prefer Julia over Python/R: Turing.jl for Bayesian statistics; Agents.jl for agent-based modelling; DifferentialEquations.jl for ODE solving.
I would much rather data-munge tabular data in Julia (DataFrames.jl) than Python, though R is admittedly quite nice on this front.
Personally I reach for Julia when I want to use one of the previous packages, or something which I want to code up from scratch, where base Julia is much preferable to me than numpy.
But, there are still reasons I reach for Julia.
Interesting packages where I prefer Julia over Python/R: Turing.jl for Bayesian statistics; Agents.jl for agent-based modelling; DifferentialEquations.jl for ODE solving.
I would much rather data-munge tabular data in Julia (DataFrames.jl) than Python, though R is admittedly quite nice on this front.
Personally I reach for Julia when I want to use one of the previous packages, or something which I want to code up from scratch, where base Julia is much preferable to me than numpy.