I'm currently working through Week 5 of Andrew Ngs Machine Learning course on Coursera, which goes through the backprop algorithm for basic neural networks. Whilst trying to derive the formulae he gave in the lectures, I noticed that the formula for $\delta^L$, "error" of last activation layer, is slightly different to that derived in http://neuralnetworksanddeeplearning.com/chap2.html.
In Andrew's, it seems like there is no inclusion of the partial derivative da/dz, or $\sigma'(z)$, only the dC/da part.
However Michael Nielson does include that term:
Is this difference significant and why does it arise? Is it because the derivation Nielson goes through defines the Cost using the mean square errors, whereas Andrew Ng defines the cost using the -ylog(h(x))... one? Also will Nielson's equations score full marks on the Ng's assignment?
Thank you for reading.