dex-lang
post-rfc
dex-lang | post-rfc | |
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25 | 27 | |
1,538 | 2,186 | |
0.5% | - | |
8.8 | 2.3 | |
2 days ago | 10 months ago | |
Haskell | ||
BSD 3-clause "New" or "Revised" License | Creative Commons Attribution 4.0 |
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dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
See: https://github.com/google-research/dex-lang/pull/969
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
post-rfc
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Haskell in Production: Standard Chartered
That's what it's best for, but personally I use it for everything. If I ever get into low-level code I'll probably use Rust though.
You can confirm that parsers/tokenizers is ranked "best in class" here though:
https://github.com/Gabriella439/post-rfc/blob/main/sotu.md
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Recommendations for well informed, up-to-date guide to Haskell backend engineering
Note that this is ported from here: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md which comes with more exposition.
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I want to learn Haskell, but...
State of the Haskell Ecosystem
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Why are haskell applications so obscure?
According to State of the Haskell ecosystem, Haskell is THE language of choice for implementing compilers, and THE language of choice for writing parsers. Thus, it is not surprising to see more Haskell projects from those particular categories than from other categories.
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base case
This is great for understanding what libraries to use in the Haskell ecosystem: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md
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Haskell for beginners
In particular, I got comfortable reading hackage documentation to understand quickly how to use libraries (aeson, megaparsec, mtl, pipes, etc), got comfortable with the ecosystem (this helped: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md), got comfortable with the main language idioms and features (https://smunix.github.io/dev.stephendiehl.com/hask/tutorial.pdf) and got comfortable with simple things that for some reason had confused me before (case, \case, let).
- What can I do in Haskell? UwU
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Is there "Are We <#$%&> Yet" type of websites for Haskell?
Gabriella Gonzalez has a great doc that is reasonably up-to-date, sounds similar to what you're looking for? https://github.com/Gabriella439/post-rfc/blob/main/sotu.md
- What I wish I had known about voice feminization from the beginning
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Haskell for Artificial Intelligence?
With that being said, Python is without a doubt the best option, and I'd also be very interested to read the articles you found that say that Python is not a good choice because it's been the industry standard for a long time now. Data science and machine learning are one of the areas where the Haskell ecosystem is not as strong as other languages, but libraries and tools do exist. There's a great list of Haskell resources by domain here, and as you can see, there are Haskell bindings to tensorflow and pytorch, along with other libraries that support common data science programming.
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
ihp - 🔥 The fastest way to build type safe web apps. IHP is a new batteries-included web framework optimized for longterm productivity and programmer happiness
futhark - :boom::computer::boom: A data-parallel functional programming language
envy - :angry: Environmentally friendly environment variables
julia - The Julia Programming Language
hackage-server - Hackage-Server: A Haskell Package Repository
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
rlua - High level Lua bindings to Rust
hasktorch - Tensors and neural networks in Haskell
awesome-haskell - A collection of awesome Haskell links, frameworks, libraries and software. Inspired by awesome projects line.
CIPs
hoogle - Haskell API search engine