#2 ML … Gradient Descent
!! Getting stuck very early on may be avoided by taking different values for y=mx+c and then taking the min of all the diff. cases!! (???)
Q- What are the different methods to avoid stuck at local-minima? Is there a single good method which will never stuck at local-minima, given a good enough time?
!1 first m = (last h — first h)/(last x — first x)
first c = sum(h(x)) / N
!!!2 in each step of optimization
take the best of a)m=m-alpha(fm) ; c = c-alpha(fc)
b)m = m-alpha(fm) ; c = c
c)m = m ; c = c- alpha(fc)
Q- Where do I get good, reliable and working data for Gradient Descent(to start) ?