autodiff
There are 165 repositories under autodiff topic.
tracel-ai/burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
google/tangent
Source-to-Source Debuggable Derivatives in Pure Python
coreylowman/dfdx
Deep learning in Rust, with shape checked tensors and neural networks
autodiff/autodiff
automatic differentiation made easier for C++
DiffSharp/DiffSharp
DiffSharp: Differentiable Functional Programming
lmfit/uncertainties
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
dlsys-course/assignment1-2018
Assignment 1: automatic differentiation
google/autobound
AutoBound automatically computes upper and lower bounds on functions.
leopard-ai/betty
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
rsokl/MyGrad
Drop-in autodiff for NumPy.
maciejkula/wyrm
Autodifferentiation package in Rust.
gdalle/DifferentiationInterface.jl
An interface to various automatic differentiation backends in Julia.
JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
hikettei/cl-waffe2
[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
TilliFe/Infermo
Tensors and dynamic Neural Networks in Mojo
can-lehmann/exprgrad
An experimental deep learning framework for Nim based on a differentiable array programming language
sradc/SmallPebble
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.
lawmurray/Birch
A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.
pierreablin/autoptim
Automatic differentiation + optimization
PennyLaneAI/catalyst
A JIT compiler for hybrid quantum programs in PennyLane
pymc-devs/sunode
Solve ODEs fast, with support for PyMC
JamesYang007/FastAD
FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
alexshtf/autodiff
A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.
ml-for-gp/jaxgptoolbox
Geometry processing utilities compatible with jax for autodifferentiation.
songlei00/easytorch
基于Python的numpy实现的简易深度学习框架，包括自动求导、优化器、layer等的实现。
invenia/Nabla.jl
A operator overloading, tape-based, reverse-mode AD
t4minka/ccml
simple autodiff library
metrumresearchgroup/Torsten
library of C++ functions that support applications of Stan in Pharmacometrics
AkiRusProd/numpy-nn-model
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
JuliaDiff/ChainRulesTestUtils.jl
Utilities for testing custom AD primitives.
tiandiweizun/autodiff
200行写一个自动微分工具
LouisDesdoigts/dLux
Differentiable optical models as parameterised neural networks in Jax using Zodiax
yizhang-yiz/fazang
Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.
JAX-DIPS/JAX-DIPS
JAX-DIPS is a differentiable interfacial PDE solver.
CMU-IDeeL/new_grad
A new lightweight auto-differentation library that directly builds on numpy. Used as a homework for CMU 11785/11685/11485.
karanchahal/buildTensorflow
A lightweight deep learning framework made with ❤️