Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
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Updated
May 16, 2024 - Python
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Drop-in autodiff for NumPy.
A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
Yaae: Yet another autodiff engine (written in Numpy).
Assignments for Data Intensive Systems for Machine Learning Coursework
Experiments with forward gradients on optimization test functions
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.
Yet another tensor automatic differentiation framework
toydl: toy deep learning algorithms implementation, backend with self implement toy torch
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
zapnAD: An auto-differentiation package.
A toy forward-mode autodiff utility written in Python
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