$1.[$Sec $11\mathrm{.}3]$ Given a set of n data points $($xi $,$ yi$),$ a line of best fit can be used to predict the output for additional input values. x y The gradient descent algorithm can be used to find the optimal line y $=$ wx $+$ b$.$ The algorithm minimizes a cost function J$($w$,$ b$)$ which measures the distance between the n points and a line y $=$ wx $+$ b$.$ Here we apply the mean squared error formula: J$($w$,$ b$)=1$ n Xn i$=1($yi $($wxi $+$ b$))2.$ The optimal coefficients w and b can be found by minimizing J$($w$,$ b$).$ Find $$J$/$w and $$J$/$b$.$