Matrix, Dimensions.
special case of this is when l is equal to 0, in which case A^[0], which is equal to just your training set input features x is going to be equal to n^[0] by m as expected. Of course, when you’re implementing this in back-propagation, we’ll see later you end up computing dz as well as da. This way,