I saw this implementation of backpropagation in MATLAB, where the loss function used is MSE, and the last layer's activation function was sigmoid.
I denoted the portions of the formula for what I thought they represented.
.* represents an element wise multiplication in MATLAB. I tried to expand this for other cases, where I used Softmax as the last layer's activation, and categorical cross entropy for the loss. Since the implementation above requires the two parts to be the same shape, and the backwards pass of Softmax gives the 2D Jacobian for a 1D Vector, how would I element-wise multiply this with the loss function's derivative, another Vector? Would taking the dot product give me the result I need?