Deep learning in Rust, with shape checked tensors and neural networks

automatic differentiation made easier for C++

Tensors and dynamic neural networks in pure Rust.

Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.

Drop-in autodiff for NumPy.

FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.

Differentiate python calls from Julia

200行写一个自动微分工具

Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.

A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.

[wip] Lightweight Automatic Differentiation & DeepLearning Framework implemented in pure Julia.

Yaae: Yet another autodiff engine (written in Numpy).

Forward mode automatic differentiation for Fortran

A minimalist neural networks library built on a tiny autograd engine

Algorithmic differentiation with hyper-dual numbers in C++ and Python

Scala embedded universal probabilistic programming language

C++20 numerical and analytical derivative computations

Chemical Explosive Mode Analysis for computational/experimental combustion diagnostics using Julia SciML features

Assignments for Data Intensive Systems for Machine Learning Coursework

Experiments with forward gradients on optimization test functions