i) 2pt - Show the weights and bias of a single neuron that computes the OR function of its binary inputs, x and y. OR (x,y)=1 if one or more of the inputs is 1 iii) 5pt - True or False - circle your answer. 1pt - True or False - Multilayer feedforward network with at least one hidden layer are universal approximators (i.e. they can learn any given function with arbitrary accuracy). 1pt - True or False - Using 3 or more hidden layers is necessary when the problem is very difficult or discontinuous. 1pt - True or False - There are some Boolean functions that can not be implemented with a one-hidden-layer feedforward network? 1pt True or False - You need to use a linear activation function at the output unit, for regression problems. 1pt - True or False - A one hidden layer feedforward network using only linear activation functions is equivalent to a network with no hidden layers (i.e. there is no gain in using the hidden layer). iv) 2pt - Give the formula and/or explain the gradient of a function f of 3 variables, x,y, and z. 4pt Compute the gradient magnitude for y(x,y)=3x2+xyy at the point [13]T. Show your calculations