@tebu1783: if there's not suitable LTS, would cabal work?
I'm a Nix advocate, I'll try to keep down the zealotry to a minimum though
Thanks for the response, @ocramz_:matrix.org
That reminded me of the classical essay The Lisp Curse. I thought this wouldn't be the case for Haskell because it's way more restricted than Lisp (in the sense that it has no macros)... But the "Haskell Curse" is still caused by the enormous amount of language extensions and new advanced features built on top of those?
That reminded me of the classical essay The Lisp Curse. I thought this wouldn't be the case for Haskell because it's way more restricted than Lisp (in the sense that it has no macros)... But the "Haskell Curse" is still caused by the enormous amount of language extensions and new advanced features built on top of those?
@man_of_letters:mozilla.org @b:chreekat.net I haven't given cabal a go, but now you mention it, I should try using Nix and cabal as a last resort, too :-)
Fwiw I don't feel like the number of language extensions is a problem, especially with the GHC20xx concept that is now available. But the fragmentation of the "standard" library is a problem
And the other problem as far as data goes is the Num type class
see? that's exactly what I meant above: 2 users, 2 orthogonal opinions.
IMO the haskell type system should simply be bypassed for data/ML workflows. With metaprogramming, generics and so on. Stuff like type families and singletons is simply a pain in the ass at scale.
this is my rationale for 'heidi' and my recent work on tensor compilation
Yeah I'm thinking the same thing, in that the boilerplate of type decorations is just a tremendous pain in the ass sometimes. And I agree that opening the doors to over engineering is probably not the right idea in this situation
My personal experience in this domain was at university studying numerical analysis and computational physics. For some dumb reason I really wanted to use C for everything at first. When I got around to anything using matrices, though, the boilerplate just killed me. I switched to octave. Sure, having a bunch of math stuff built in was useful, but what really saved me was a compact syntax built for the problem domain.
That's why I'm not sure just using raw Haskell would be the right idea.
I never got around to learning python, so I'm curious to know in what ways it was flexible enough to get heavy adoption for mathematical programming
chreekat: python is great for doing science because it runs your code no matter what. No matter what they say about pre-registering experiments, scientists just love to tinker and make up hypotheses after the fact
haskell is the opposite, it forces you to formalize the data domain very early. And the more type stuff you add to the domain, the harder it becomes to refactor
Hey guys, I'm looking for simple models in Haskell to generate random unique names (as in first names), just to identify stuff (as random UUIDs would, but in a pronounceable manner). Grenade seems to be able to train models that could do that, but maybe it's overkill, and also it doesn't seem really maintained. Any idea? I'd like something light that generates serializable models so it can easily be embedded in a Haskell application
@YPares: hi! what kind of neural networks are these (in newbie terms: fully connected, convolutional, recurrent, etc.)? Do you have in mind some classic model described somewhere in detail?
also, is the pronounceability learned by imitation and not verified, or is there a loss function that really enforces that during the training? (pardon me if that's trivial to google)
chreekat: python is great for doing science because it runs your code no matter what. No matter what they say about pre-registering experiments, scientists just love to tinker and make up hypotheses after the fact
I must say, I've had some horrible experiments where I run Python code for hours and then finally it prints out:
Traceback (most recent call last):
File "boop.py", line 1, in <module>
print(x)
NameError: name 'x' is not defined
Though that's completely consistent with what you said :P
@ocramz_:matrix.org
and they say Haskell compilation takes a long time...
anton_5: backprop is very handy. I've used it a few times
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oh, cool; for ML, as in neural nets, backprop should be great; at least, regarding performance on CPU and if your nets have large tensors in them, as opposed to very many smaller ones (it uses blas/lapack under the hood)
Oh, nice, good to know, thanks! I wonder if it can work with the accelerate as well.
Looks like accelerate has bindings to cublas, so, in theory, it should be possible to substitute regular blas in the backprop for cublas.
Or use the arrayfire bindings.
mentions library https://gitlab.com/sacha-sokoloski/goal used in https://elifesciences.org/articles/64615 , probably of interest to some datahaskellers :)
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Hi! I come from a pure math background with an interest in data. I recently stumbled upon haskell and really found it cool. I feel that a really nice way for me to get familiar and learn more about haskell, work on some cool stuff and contribute to the community. How do I go about all this? Thanks :)
I have experience in coding with c/c++/openmp/mpi/cuda/python and other basic ml libraries
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Welcome! So, would you like to contribute to a Haskell data-related project? Right now, or after learning Haskell in a conventional way? Haskell does take some adjusting, though the challenging experience is described as beneficial by the survivors.
there was a very recent talk on accelerate https://www.youtube.com/watch?v=FzfofFPOGOs&list=PLOvRW_utVPVnqp24VsF0wiIRa-m9oWrox&index=2
Just a question, if my company says that any work generated by the associate arising out/in connect of my connection with the company counts as confidential stuff, then would I be able to contribute to these kind of projects or would I need to talk about these things with my company first? I'm new to all these things, so please don't mind these basic questions.
re geometry, this is not computational geometry, I think, but a bit related: https://gitlab.com/sacha-sokoloski/goal
re your company, IANAL, but if you are not doing it in company time, they should not have a say, and if you do, they probably should