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The Evolution of Hyperparameters in a Neural Network
The best parameters are those that allow the neural network to generalize quickly after seeing a few pieces of data. And so does that mean that validation is better for the smaller subnets and less good for the larger subnets? That's also an artifact of this kind of learning speed perspective. So basically, on the higher up the chain, so to speak, that you go, I guess you condition on more data, and therefore the effect of the validation data becomes less.