Neural Networks, Gradient Descent.
need to take partial derivatives of the function for each variable. This is vector calculus in neural networks context and it is almost all the same concepts written out as matrices. The gradient vector of such a function is the Jacobian matrix J(F). For our example function F(x, y) = (x² + y²,