How to visualize the minimum of a cost function which has more than 2 weights as parameters? Like for example at this image , this is how a cost function looks like given it has two weights as parameter . I understand how we can use gradient descent to find the minimum of this function and the analogy which made me understand it was hill climb analogy as choosing the number of steps taken to reach the minimum cost function . So how can I visualize the minumim of the cost function which got more weights as parameters with such intuition? 

It's impossible to "losslessly" visualize a very high dimensional space in just 2 dimensions. One approach is to randomly pick two unit vectors $u, v$ in $\mathbb{R}^n$, and then you can "project" your loss $f(\vec x)$ into the easily visualized, two dimension $g(a,b) \mapsto f(au+bv)$. Hao Li et. al present a slightly more refined version of this which produces pretty interesting results. 
