Differentiable computing on R^N metric spaces
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Updated
Sep 24, 2022 - Python
Differentiable computing on R^N metric spaces
TensorFlow implementation of differentiable LQ matrix decomposition for all matrix orders.
A simple library for building computational graphs with autodiff support.
A simple and pythonic deep learning framework
An automatic differentiation library written in Python with NumPy vectorization.
Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers
Tiny automatic differentiation (autodiff) engine for NumPy tensors implemented in Python.
A reverse-mode automatic differentiation package
A toy automatic differentiation engine written in Python.
A toy forward-mode autodiff utility written in Python
Automatic differentiation in 16 lines of code.
Simple automatic differentiation implementation in python
Version 2.0 of Kineverse, a framework for modeling kinematics for robotic manipulation and control.
Implementation of propagation of uncertainty using dual numbers
With awesome options like micrograd or tinygrad out there, why not write another small autodiff engine? ¯\_(ツ)_/¯
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