Gradient-based explanations estimate the contribution of input $i$ towards output $o$ by computing the partial derivative of $o$ with respect to $i$. This operation is particularly convenient for neural network (NN) based models because these partial derivatives can be computed for any layer using a single call to auto-differentiation, which most deep learning engines provide out-of-the-box.