ReverseDiff implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object) using reverse mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy.