Neural Networks, Gradient Descent.
is computationally expensive. In gradient descent, we do not use it. So it is an approximation. This is why it is also called a first-order approximation. For example, the above graph plots the function (x²-16)². The second derivative of this is 12x²-64. This means there exists some x range where