You probably can... but is that really the issue? I think the problem isnt that you cant solve problems with small amounts of data; its that you can't solve 't

You probably can... but is that really the issue? I think the problem isnt that you cant solve problems with small amounts of data; its that you can't solve 'the problem' at a small scale and then just apply that solution at large scale... and that's not what people want or expect. People expect that if you have an industrial welder than can assemble areoplanes (apparently), then you should easily be able to check it out by welding a few sheets of metal together, and if it welds well on a small scale, it should be representative of how well it welds entire vehicles. ...but thats not how DNN models work. Each solution is a specific selection of hyperparameters for the specific data and specific shape of that data. As we see here, specific even to the volume of data available. It doesnt scale up and it doesn't scale down. To solve a problem you just have to sort of.... just mess around with different solutions until you get a good one. ...and even then, you've got no really strong proof your solution is good; just that its better than the other solutions you've tried. Thats the problem; its really hard to know when DNN are the wrong choice, vs. you're just 'doing it wrong'