How does sigmoid function derivative works for backpropagation
I am trying to understand formula behind backward propagation.
So if our last layer is sigmoid we can calculate dZ = A−Y
But if we have a layer in middle of neural network, it is calculated like dZ = dA⋅A⋅(1−A).