JuliaDiff/ForwardDiff.jl

This is stars and forks stats for /JuliaDiff/ForwardDiff.jl repository. As of 29 Apr, 2024 this repository has 822 stars and 135 forks.

ForwardDiff.jl ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Here's a simple example showing the package in action: julia>...