The fact that there are people "getting their jimmies up" on questions of training massively paramterized statistical models on tiny amounts of data should tell you exactly where we are on the deep-learning hype cycle. For a while there, SVMs were the thing, but now the True Faithful have moved on to neural networks.
The argument this writer is making is essentially: "yes, there are lots of free parameters to train, and that means that using it with small data is a bad idea in general, but neural networks have overfitting tools now and they're flexible so
you should use them with small data anyway." This is literally the story told by the bulleted points.
Neural networks are a tool. Don't use the tool if it isn't appropriate to your work. Maybe you can find a way to hammer a nail with a blowtorch, but it's still a bad idea.
The fact that there are people "getting their jimmies up" on questions of training massively paramterized statistical models on tiny amounts of data should tell you exactly where we are on the deep-learning hype cycle. For a while there, SVMs were the thing, but now the True Faithful have moved on to neural networks.
The argument this writer is making is essentially: "yes, there are lots of free parameters to train, and that means that using it with small data is a bad idea in general, but neural networks have overfitting tools now and they're flexible so you should use them with small data anyway." This is literally the story told by the bulleted points.
Neural networks are a tool. Don't use the tool if it isn't appropriate to your work. Maybe you can find a way to hammer a nail with a blowtorch, but it's still a bad idea.