dex-lang
Pytorch
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dex-lang | Pytorch | |
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25 | 336 | |
1,534 | 77,783 | |
0.1% | 2.4% | |
8.8 | 10.0 | |
20 days ago | 3 days ago | |
Haskell | Python | |
BSD 3-clause "New" or "Revised" License | BSD 1-Clause License |
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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
Pytorch
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
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Building a GPT Model from the Ground Up!
import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Element-wise vs Matrix vs Dot multiplication
In PyTorch with * or mul(). ` or mul()` can multiply 0D or more D tensors by element-wise multiplication:
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
futhark - :boom::computer::boom: A data-parallel functional programming language
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
julia - The Julia Programming Language
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
hasktorch - Tensors and neural networks in Haskell
flax - Flax is a neural network library for JAX that is designed for flexibility.
CIPs
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
tutorials - PyTorch tutorials.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more