I want to understand how the backpropagation algorithm would work on a neural network with multiple outputs.
More specifically, I have a network with 21 binary (0/1) outputs and I want to minimize the number of outputs that I get correctly; in other words, I want to minimize the hamming distance between the output vector and the desired vector.
How does the loss function work here? How do I backpropagate the error and update the weights? I know this might be long to explain so I'm also happy with links to some good references that I could read on the matter.