A simple library for building computational graphs with autodiff support.
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Updated
Jul 31, 2019 - Python
A simple library for building computational graphs with autodiff support.
Simple automatic differentiation implementation in python
A brief (and inaccurate) history of derivatives, with a brief (and incomplete) Python implementation
Realization of models from existing papers
My implementation of Andrej Kaparthy's Micrograd library for back propagation and simple neural net training
micrograd (smol autodiff lib by @karpathy) ported into various languages
A toy forward-mode autodiff utility written in Python
zapnAD: An auto-differentiation package.
c++ header-only library for scientific programming.
A simple and highly extensible Computational graph library written in C++ with the support of auto diff.
Simple neural network and automatic differentiation implementation
Yet another automatic differentiation engine to perform efficient and analytically precise partial differentiation of mathematical expressions.
Dualitic is a Python package for forward mode automatic differentiation using dual numbers.
A Micrograd inspired (and largely copied) small autodiff engine.
Yet another tensor automatic differentiation framework
Lightweight automatic differentiation and error propagation library
Testing capabilities of Trilinos-Sacado in combination with e. g. tensors
🚢 Portable development environment for Enzyme
A tiny autograd adapter implemented in Java with PyTorch-like API.
toydl: toy deep learning algorithms implementation, backend with self implement toy torch
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