We have mainly focused on squared loss, but there are other interesting losses in machine learning. Consider the following loss function which we denote by $0($z$)=$ max$(0,-2).$ Let S be a training set $($x$+,$ y$),...,($X$",$y$)$ where each ri ER$"$ and yi $\{-1,1\}.$ Consider running stochastic gradient descent $($SGD$)$ to find a weight vector w that minimizes L$-10($y$".$ w$1$x$).$ Explain the explicit relationship between this algorithm and the Perceptron algorithm. Recall that for SGD$,$ the update rule when the ith example is picked at random is Wnew $=$ Wold $$ no $($y$*$wz$).$