What a profound criticism. If data scientists can't use Julia then they're not great data scientists - you have RCall and PyCall, Flux, Knet, MLJ, plotting libraries, etc. It has built-in and user-defined types, threads, multiple dispatch, ...
"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."
What a great statement, its not that they can't its that they don't want to use it.
I really tried to like Julia, wrote it for months, but it misses the mark. I've been working in analytics for 10 years and of everyone I know who's tried it I only know a couple people who like Julia.
Thats a problem, and you can see it in the adoption numbers. The Julia community keeps wanting to bury their head in the sand and pretend its not which is fun.
fishmaster|5 years ago
What a profound criticism. If data scientists can't use Julia then they're not great data scientists - you have RCall and PyCall, Flux, Knet, MLJ, plotting libraries, etc. It has built-in and user-defined types, threads, multiple dispatch, ...
"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."
mountainriver|5 years ago
I really tried to like Julia, wrote it for months, but it misses the mark. I've been working in analytics for 10 years and of everyone I know who's tried it I only know a couple people who like Julia.
Thats a problem, and you can see it in the adoption numbers. The Julia community keeps wanting to bury their head in the sand and pretend its not which is fun.