Just to note: although the models discussed in this blog can be arbitrary deep and rely on multilayer perceptrons, the principles of learning are completely dif

Just to note: although the models discussed in this blog can be arbitrary deep and rely on multilayer perceptrons, the principles of learning are completely different then in other contemporary deep learning: the error is injected at every level in a distributed way. There is no vanishing gradient, because there is no necessity to propagate anything end to end.