michael_nielsen

I meet lots of people who tell me fatalistically (& often despondently) that it's near impossible to do important work on neural nets today, unless you have huge compute and huge data sets.

It seems to me this is based on a bad model of how scientific progress gets made.

You can see this in part by looking at historically important discoveries: what do backpropagation, conv nets, Alexnet, GANS, LSTMs, ReLUs all have in common? All were developed with relatively small compute, and small data sets.

In science, the most important progress often comes from better questions and better ideas, not better equipment (in this case, more computational power and data).