Backpropagation is a leaky abstraction
I do think the complaint on having to write the backward pass seems especially shallow; finding out they were working with numpy makes it even more so (since numpy takes the pain out of the matrix operations). IIRC, when I took the ML Class in 2011, we used Octave, but Ng had us first write stuff "the hard way" - so we'd understand what was going on later when we used Octave's methods.<p>Something about this article as a whole, though, does raise a question that I've been wondering about, and I want to present it here for a bit of discussion (maybe it needs its own thread?):<p>Does anyone else here think that the current approach to neural networks has some fundamental flaws?<p>Now - I'm not an expert; call me an interested student right now, with only the barest of experience (beyond the ML Class, I also took Udacity's CS373 course, and I am also currently enrolled in their Self-Driving Car Engineer nanodegree program).<p>I understand that what we currently have and know does work. What I mean by that is the basic idea of an artificial neural network using forward and back-prop, multiple layers, etc (and all the derivatives - RNN, CNN, deep learning, etc). I understand the need and reasoning behind using activation functions based around calculus and derivatives and the chain-rule, etc (though I admit I need further education in these items).<p>But something nags at me.<p>All of this, despite the fact that it works and works well (provided all your tuning and such is right, etc), just seems like it is over-complicated. Real neurons don't use calculus and activation functions, nor back-propagation, etc in order to learn. All of those things in an ANN are just abstractions and models around what occurs in nature.<p>Maybe (probably?) I am wrong - but it seems like what nature does is simpler. Much less power is used, for instance, and the package is much more compact. I just have this feeling that in some manner we may have gone down a path that while it has produced a working representation, that representation is overly complex, and had we took another approach (whatever that might be?), our ANNs would look and work much differently - perhaps even more efficiently.<p>About the only alternatives I have heard about otherwise have been things like spike-train neural networks, and some of the other "closer to nature" simulation (of ion pumps and real synapses, etc). Still, even those, while seemingly closer, also have what appears to be too much complexity.<p>I'm probably just talking out of my nether regions as a general n00b to the field. I do wonder, though, if there might be another solution, seemingly out in "left-field" that might push things forward, if someone was willing to look and experiment. It is something I plan to look into myself, as I find time and such between lessons and other work for my current learning experience.