can't you kind of say this about all ML? That the main driver in ML is 99% the quality of the training data and 1% the specific details of the neural networks used?
For most real-world applications of ML, yes. Of course, what happens in ML-research is different (where e.g. new networks for new modalities are invented).
But back to the topic, I bet the kid didn't even invent their own neural network topology, but just pulled a predefined network from a library, perhaps without even knowing it. Which is ok, because that is how most people use ML.
But back to the topic, I bet the kid didn't even invent their own neural network topology, but just pulled a predefined network from a library, perhaps without even knowing it. Which is ok, because that is how most people use ML.