Backpropagation, Mathematics, Calculus, Machine Learning, Neural Networks.
instead. Since each activation is multiplied by a corresponding weight, we find that each of the activation’s derivatives is just it’s corresponding weight. Therefore, ∂z2 / ∂a1 is equal to a 1 x 3 vector like this, which we actually transpose just for dimension compatibility reasons when we get to