The loading problem formulated by J. S. Judd seems to be a relevant model for supervised connectionist learning of the feedforward networks from the complexity point of view. It is known that loading general network architectures is NP-complete (intractable) when the (training) tasks are also general. Many strong restrictions on architectural design and/or on the tasks do not help to avoid the intractability of loading. Judd concentrated on the width expanding architectures with constant depth and found a polynomial time algorithm for loading restricted shallow architectures. He suppressed the effect of depth on loading complexity and left as an open prototypical computational problem the loading of easy regular triangular architectures that might capture the crux of depth difficulties. We have proven this problem to be NP-complete. This result does not give much hope for the existence of an efficient algorithm for loading deep networks.