Do deep neural networks learn slower with the addition of more hidden layers? The number of hidden layers increases the number of weights, also increases the terms in the back-propagation algorithm, i.e.  more derivatives, hence more computation. Can we say that neural networks learns slower with the addition of more hidden layers?

It'll have more weights and in each new added layer you're getting more complex functions that's in NN sense your function will be a more complex composition of another functions and your output will be hardier to optimise so its convergence will be slower. I'd say that convergence getting slower is mainly due to complexity composition, the number of weights will not change its speed very much.
