This list contains programming languages that are obviously in use and some which are proposed for usage in machine learning. A fundamental issue with programming languages remains the same. There are n programming languages available of which none is considered perfect so someone invents a new one that fixes a couple things. We end up with n=n+1 programming languages. Then someone else invents another programming language and again n+1…. .
- C++
- underlying language of many python packages and certainly the most used language for any deployment case that requires low latency
- CUDA
- most ignored language as basically everything that runs on GPUs is somewhat compiled cuda coda (sometimes via some abstractions layers)
- Cython
- abstraction language to create C extensions for Python easily (many python packages - e.g. cupy use it to achieve at least some decent performance)
- dex
- research language for typed, functional array processing
- Julia
- certainly more in use for “scientific machine learning”
- Python
- most likely the defactor language for machine learning research, teaching and deployment
- R
- in decline, seems to get replaced by python a lot; still more relevant for more classical statistics applications
- Swift
- used for deployment of machine learning solutions in the Apple universe
- Zig
- claims to be a simple language that is claimed to be useful for machine learning