Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
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Updated
Jul 14, 2025 - Python
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Drop-in autodiff for NumPy.
XLuminA, a highly-efficient, auto-differentiating discovery framework for super-resolution microscopy.
A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
Yaae: Yet another autodiff engine (written in Numpy).
A differentiable underwater vehicle dynamics in body and ned(euler & quaternion).
Experiments with forward gradients on optimization test functions
Assignments for Data Intensive Systems for Machine Learning Coursework
Fork of Matt Loper's autodifferentiation framework for Python
Tiny automatic differentiation (autodiff) engine for NumPy tensors implemented in Python.
Dualitic is a Python package for forward mode automatic differentiation using dual numbers.
TensorOps - A Work-In-Progress Autodiff Library
Yet another tensor automatic differentiation framework
PyPIC3D is a 3D particle-in-cell code written in Python using Jax.
ToyDL: Deep Learning from Scratch
A toy forward-mode autodiff utility written in Python
A simple library for building computational graphs with autodiff support.
Realization of models from existing papers
zapnAD: An auto-differentiation package.
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