I was taking an online course and saw linear regression being by gradient descent The intuition behind why the method would work seemed plausible.
I tried understanding normal equation as to why setting individual partial derivates to 0 and solving the equations give the optimal values of theta, but that didn't ring a bell.
- Why setting partial derivatives to zero, and solving the equations gives optimal value of theta.
I also went through the following link. The part about
The minimum is determined by calculating the partial derivatives of S(β1,β2) with respect to β1 and β2 and setting them to zero
is still dicey to me.
Why would it work. Why would it give the values of β1 and β2 for minimized cost function.
- If we have multiple local optima, would the normal equation method give the global optima? If so, why?