Neural Networks, Gradient Descent.
intuitively we know from this simple equation that f(x) is minimum at x =0 and the value of y at x=0 is the answer/minimum of the function. This is because x is squared and if you substitute even negative values the value of y is just going to increase. So zero is the minimum. However, we do not