Neural Networks & Machine Learning. Please explain in great details and show work clearly. Thanks

(10 pts) Suppose the we have 2 samples of image (low-res gray scale) labeled data to be used in logistic regression as shown below: x(1)=25121008312149,y(1)=1x(2)=562008731210,y(2)=0 LoR Forward Propagation Z=WTX+bA=(Z) LoR Backward Propagation dZ=AYdW=m1XdZTdb=np,sum(dZ)/mW:=WdWb=bdb With employing the logistic regression algorithm above, a) What are the dimension shapes of X,Y,W,b,A, and Z ? b) If reshaping/flattening a matrix means stacking the columns (left to right) on top of each other, what do the input feature matrix X and output vector Y look like? Write out them in matrix/vector form with numerical values. b) If reshaping/flattening a matrix means stacking the columns (left to right) on top of each other, what do the input feature matrix X and output vector Y look like? Write out them in matrix/vector form with numerical values