There are 1 repository under autodifferentiation topic.
Deep learning in Rust, with shape checked tensors and neural networks
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
Differentiate python calls from Julia
Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.
[wip] Lightweight Automatic Differentiation & DeepLearning Framework implemented in pure Julia.
Forward mode automatic differentiation for Fortran
Algorithmic differentiation with hyper-dual numbers in C++ and Python
C++20 numerical and analytical derivative computations
Scala embedded universal probabilistic programming language
Chemical Explosive Mode Analysis for computational/experimental combustion diagnostics using Julia SciML features
Assignments for Data Intensive Systems for Machine Learning Coursework
Automatic differentiation: A tool that allows you to calculate multivariable equations, vectors, matrices, and more. All done in C++, no libraries!
Experiments with forward gradients on optimization test functions
A tiny autograd library made for educational purposes.
F-1 method
yacc lex for reversed automatic differentiation
Fork of Matt Loper's autodifferentiation framework for Python
Reversed mode second order automatic differentiation for python (WIP)
Dualitic is a Python package for forward mode automatic differentiation using dual numbers.
Tiny automatic differentiation (autodiff) engine for NumPy tensors implemented in Python.
🚢 Portable development environment for Enzyme
Simple implementation of reverse-mode automatic differentiation on numpy arrays
toydl: toy deep learning algorithms implementation, backend with self implement toy torch
Lightweight automatic differentiation and error propagation library