DiffSharp
Pytorch
| DiffSharp | Pytorch | |
|---|---|---|
| 2 | 340 | |
| 574 | 78,205 | |
| 1.0% | 1.4% | |
| 4.6 | 10.0 | |
| 22 days ago | about 8 hours ago | |
| F# | Python | |
| BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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DiffSharp
- Automatic differentiation in a lot more than 38 lines of F#
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Neural Networks Fsharp
Yes. You can use TensorFlow.NET or DiffSharp.
Pytorch
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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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():
What are some alternatives?
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
ocaml-torch - OCaml bindings for PyTorch
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
corgi - A neural network, and tensor dynamic automatic differentiation implementation for Rust.
flax - Flax is a neural network library for JAX that is designed for flexibility.
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
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
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java