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 # 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.