The Elements of Differentiable Programming

1. ForwardDiff.jl

   1 4 932 6.5 Julia

   Forward Mode Automatic Differentiation for Julia

   

   You seem somewhat obsessed with the idea that reverse-mode autodiff is not the same technique as forward-mode autodiff. It makes you,,, angry? Seems like such a trivial thing to act a complete fool over.

   What's up with that?

   Anyway, here's a forward differentiation package with a file that might interest you

   https://github.com/JuliaDiff/ForwardDiff.jl/blob/master/src/...

2. InfluxDB

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3. ceres-solver

   2 9 4,092 8.3 C++

   A large scale non-linear optimization library

   

   I can't reply to the guy saying julia is the only one. But there are others.

   Ceres uses dual numbers

   https://github.com/ceres-solver/ceres-solver/blob/master/inc...

   This library from google is used everywhere in robotics, so it's hardly some backwater little side project.

   So does c++ autodiff

4. autodiff

   3 7 1,770 6.2 C++

   automatic differentiation made easier for C++

   

5. Pytorch

   4 392 89,871 10.0 Python

   Tensors and Dynamic neural networks in Python with strong GPU acceleration

   

   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].

6. jax

   5 90 32,213 10.0 Python

   Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

   

   The dual numbers exist just as surely as the real numbers and have been used well over 100 years

   https://en.m.wikipedia.org/wiki/Dual_number

   Pytorch has had them for many years.

   https://pytorch.org/docs/stable/generated/torch.autograd.for...

   JAX implements them and uses them exactly as stated in this thread.

   https://github.com/google/jax/discussions/10157#discussionco...

   As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.

7. SaaSHub

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