$2.$ We have mainly focused on squared loss, but there are other interesting losses in data$-$mining. Consider the following loss function which we denote by $0(2)=$ max$(0,-2).$ Let S be a training set $(2,$ y$),...,$x$,$y$)$ where each r ER$"$ and y E$\{-1,1\}.$ Consider running stochastic gradient descent $($SGD$)$ to find a weight vector w that minimizes $12$ oly. wr$).$ Explain the explicit relationship between this algorithm and the Perceptron algorithm. Recall that for SGD$,$ the update rule on the ith example is Wnew $=$ wold $-706($y$\text{'}$w$?$:$)$