Can I come up with a bit of criticism? This book does provide a great description of the details of the algorithm inner workings (very cute demons too). However, after reading this chapter (sorry I haven't looked at the other ones), there is still a feel of a bit of mystery about why it works, and even more why it might not work. Possibly is is covered in other parts of the book, so I apologise if this criticism is not justified. I am personally a big fan of Christopher Bishop's book Pattern Recognition and Machine Learning, where backprop is described as an architecture for efficient computation of multiple stochastic gradient descents... I was involved with NNs before, but only after understanding where the algorithm for individual neurons comes from, I could properly appreciate the benefits of backprop (and understand the drawbacks).