$(3)2.$ Suppose you have a $3-$dimensional input x$=($x$\_\{1\},$x$\_\{2\},$x$\_\{3\})=(3,1,2)$ fully connected with weights $(0\mathrm{.}6,0\mathrm{.}5,0\mathrm{.}3)$ to a neuron in the hidden layer with sigmoid as activation function. Assume the bias of hidden layer mode as $0\mathrm{.}5.$ Calculate the output of the hidden layer neuron.$(3)3.$ Differentiate the following activation functions: Sigmoid, Tanh, ReLU.$(3)4.$ Consider a linear SVM trained with n labeled points, $($X$\_\{$i$\},$y$\_\{$i$\})$ X$\_\{$i$\}\backslash$in R$^\{2\}.$ y$\_\{$i$\}\backslash$in$\backslash \{0,1\backslash \}$ resulting in k$=2$ support vectors $($k