Consider the simplest deep linear neural network that is described by the following equations: 2= w12, y = W22, with c, w1, W2, Y ER. This is indeed a very simple DNN that receives a one dimensional input, has one hidden layer with one unit, and one output. This simplicity is to ensure that the calculations are all easy. Consider the squared error loss function (y,t) = (y - t)? Part (a) Show that one can replace this 2-layer NN with a 1-layer NN (show the relation of the input I to the output y). (2 MARKS] Part (b) Compute the gradient of the loss of the 2-layer NN with respect to wi and w2. [4 MARKS] Part (c) Is the loss function of the 2-layer NN convex with respect to wi and w2 or not? Prove your claim. [4 MARKS Consider the simplest deep linear neural network that is described by the following equations: 2= w12, y = W22, with c, w1, W2, Y ER. This is indeed a very simple DNN that receives a one dimensional input, has one hidden layer with one unit, and one output. This simplicity is to ensure that the calculations are all easy. Consider the squared error loss function (y,t) = (y - t)? Part (a) Show that one can replace this 2-layer NN with a 1-layer NN (show the relation of the input I to the output y). (2 MARKS] Part (b) Compute the gradient of the loss of the 2-layer NN with respect to wi and w2. [4 MARKS] Part (c) Is the loss function of the 2-layer NN convex with respect to wi and w2 or not? Prove your claim. [4 MARKS