I worked on robot control with NNs in the early-mid nineties. Maybe seven neurons and 25 edges. No layers at all. The graph and edge weights determined by a genetic algorithm. Fun.
Mid 90s I was working on neural nets and other machine learning, based on gradient descent, with manually computed derivatives, on genomic data (from what I can recall, we had no awareness of LeCun; I didnt find out about his great OCR results until much later). it worked fine and it seemed like a promising area.
My only surprise is how long it took to get to imagenet, but in retrospect, I appreciate that a number of conditions had to be met (much more data, much better algorithms, much faster computers). I also didn't recognize just how poorly MLPs were for sequence modelling, compared to RNNs and transformers.
I mean we didn't know autodifferentiation was a thing, so we (my advisor, not me) analytically solved our loss function for its partial derivatives. After I wrote up my thesis, I spent a lot of time learning mathematica and advanced calculus.
I haven't invested the time to take the loss function from our paper and implement in a modern framework, but IIUC, I wouldn't need to provide the derivatives manually. That would be a satisfying outcome (indicating I had wasted a lot of effort learning math that simply wasn't necessary, because somebody had automated it better than I could do manually, in a way I can understand more easily).
I can't express the extent to which autodifferentiation was like a revelation to me. I don't work in ML, but in grad school around 2010 I was implementing density functional theory computations in a code that was written in Fortran 77. My particular optimization needs required computing to second derivatives. I had Mathematica to actually calculate the derivatives, but even just the step of mechanically translating the computed derivatives into Fortran 77 code would be a week of tedious work. Worse was rewriting these derivative expressions for numerical stability. The worst was realizing you made a mistake in an expression high in the tree and having to rewrite everything below. The whole process took months for a single model, and that's with chain rule depth that probably could be counted on one hand. I can't imagine deep learning making the kind of progress it has without autodifferentiation - the only saving grace is that neural networks tend to be composed from large number of copies of identical functions, and you only need to go to first derivatives.
it means that code has to read values from each layer and do some summarizing math, instead of passing layer blocks to a graphics card in one primitive operation implemented on the card.
No. I should have said "determined the partial derivatives of the weights with respect to the variables analytically". We didn't have layers- the whole architecture was a truly crazy combination of dynamic programming with multiple different matrices and a loss function that combined many different types of evidence. AFAICT nobody does any of this any more for finding genes. We just take enormous amounts of genetic data and run an autoencoder or a sequence model over it.
