Problem 4. Assume we are a looking at single convolutional layer in a convolutional neural network with a max pooling layer following it as shown below (assume a linear activation function (x)=x) kernel k \begin{tabular}{|l|l|l|} \hline 1 & 0 & 2 \\ \hline 0 & 1 & 0 \\ \hline 3 & 2 & 1 \\ \hline \end{tabular} (a) Fill in the first two elements of the output of the convolution (the rest is already provided for you). (b) What would be the output of the max pooling layer? Show the 2d matrix. (c) Assume that during back-propagation the derivative of the loss function with respect to the output of the max pooling layer is given below. What is the derivative of the loss function with respect to the output of the convolution layer? Show the 2d matrix. (d) Using that result, what is the derivative of the error with respect to w0,0 of the kernel? Problem 4. Assume we are a looking at single convolutional layer in a convolutional neural network with a max pooling layer following it as shown below (assume a linear activation function (x)=x) kernel k \begin{tabular}{|l|l|l|} \hline 1 & 0 & 2 \\ \hline 0 & 1 & 0 \\ \hline 3 & 2 & 1 \\ \hline \end{tabular} (a) Fill in the first two elements of the output of the convolution (the rest is already provided for you). (b) What would be the output of the max pooling layer? Show the 2d matrix. (c) Assume that during back-propagation the derivative of the loss function with respect to the output of the max pooling layer is given below. What is the derivative of the loss function with respect to the output of the convolution layer? Show the 2d matrix. (d) Using that result, what is the derivative of the error with respect to w0,0 of the kernel