"The email including them got lost to Meta's two-year auto-delete policy by the time I went back to look for it last year. I have a binder with a lot of them printed out, but not all of them."
RIP. If it's any consolation, it sounds like the list is at least three years old by now. Which is a long time considering that 2016 is generally regarded as the date of the deep learning revolution.
> If it's any consolation, it sounds like the list is at least three years old by now.
In my experience when it comes to learning technical subjects from a position of relative total ignorance, it's the older resources that are the easiest to bootstrap knowledge from. Then you basically work your way forward through the newer texts, like an accelerated replay of a domain's progress.
I think it's kind of obvious that this would be the case when you think about it. Just like how history textbooks can't keep growing in size to give all past events an equal treatment, nor can technical references as a domain matures.
You're forced to toss out stuff deemed least relevant to today, and in technical domains that's often stuff you've just started assuming as understood by the reader... where early editions of a new space would have prioritized getting the reader up to speed in something totally novel to the world.
That’s actually fascinating. Were there many experiments done in it back in the 00’s?
I’m just trying to imagine the things you could do with it back then. 2007 had relatively fast gpus for the time, but certainly nothing compared to today. Yet it’d certainly be enough for MNIST training, which makes me wonder what else could be done.
FWIW in 2016 I was at an ML team at Apple that had been shipping production neural networks on-device for a while already. At the everyone used an assortment of random tools (Theano, Torch, Caffe). I worked on an internal tool that originally started as a Theano fork but was closer to a modern-day Tensorflow XLA (and has since been axed in favor of Tensorflow for most teams).
RIP. If it's any consolation, it sounds like the list is at least three years old by now. Which is a long time considering that 2016 is generally regarded as the date of the deep learning revolution.