A simple automatic differentiation library in Rust.
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Updated
Mar 3, 2023 - Rust
A simple automatic differentiation library in Rust.
A prototypical, experimental framework to define and execute computational graph to train neural networks.
A simple automatic differentiation library written in Go
A pedagogical implementation of Automatic Differation on multi-dimensional tensors.
AD with Enzyme through Lulesh.
A pure-Python, PyTorch-like automatic differentiation library for education.
Like torch, but rather than seeing the light, you get burnt.
Gograd is a small automatic differentiation framework written in Go.
Mercury library for automatic differentiation
A simple forward mode automatic differentiation package
Tiny (header-only) Automatic Differentiation library for C++
A Julia package for differentiating through expectations with Monte-Carlo estimates
PyTorch Autodiff DFT-D4 Implementation.
Julia interface to the Generalised Truncated Power Series Algebra (GTPSA) library
Derivatives (mathematical) computation tools
A minimal example of reverse-mode automatic differentiation (aka backpropagation)
Slides in reveal.js, covering an introduction to PyTorch
Fwd:AD is a Rust library (crate) to perform forward auto-differentiation, with a focus on empowering its user to manage memory location and minimize copying. This repo is a mirror of https://gitlab.inria.fr/InBio/Public/fwd_ad.
An Implementation of Generalized Dual Numbers
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