[0]: https://futhark-lang.org/student-projects/duc-bsc-thesis.pdf
I'll be very interested to see where this project goes.
How did that happen? Did the trend start because the work was done by domain specialists who were not necessarily expert programmers, and C was too unergonomic for non-experts to use? Is that the reason a Skyrim modder can't get immediate feedback from a compiler in 2020 - because 25 years ago game devs thought that documenting a C API was harder than embedding a scripting language in the game engine? Or perhaps because evaluating scripts was more secure than loading third-party DLLs and exposing the game's innards to them?
Edit: also, if every CSV file came with a schema, that would be great. Even if it says that every column is of type Option<Any> - at least then I know what to expect.
Therefore,
- game scene development in games, are in scripts. - Equity derivatives and swap contracts are in Excel - scientific modeling is in python
I also agree with your other though that, dynamic loading enables 're-use' of the core framework, so those frameworks/engines become marketable technology assets so to speak.
The programmers, then in some instances, created DSLs to then auto-generate performant framework-specific code from the DSL-written user specs.
But those, usually, are quite limited and also some power users migrated to be descent (not the expert engine/core-level, perhaps) programmers
Jokes aside, see:
https://github.com/LaurentMazare/tensorflow-ocaml
Also
http://ryanrhymes.blogspot.com/2017/03/build-neural-network-...
for how you can very quickly write something from scratch with "Ocaml's numpy", owl.
https://devpost.com/software/sylvester-tf
using typed natural number dimensions for arrays, vectors, matrices, tensors et.al
“One of the ironies of today's computer programming landscape is that functional languages directly inspired by the declarative languages for expressing abstractions and equations of logic and mathematics, have been sidelined for mathematical and scientific computing in favor of imperative, dynamically-typed languages like Python and Julia.”
Sadly, I’ve found that every road leads back to Python when it comes to deep neural nets. I tried using TensorFlowSharp from F# but found it very frustrating. Only Python seems to have full bindings to the TF API. Would love to give your wrapper a try.
that demos my ongoing progress with Sylvester and I'd love your feedback. It's also available on NuGet: https://www.nuget.org/packages/Sylvester.tf/0.2.3.4 with a native TF 2 package: https://www.nuget.org/packages/Sylvester.tf.Native.Win/.
It's still WIP but coming along pretty well.
