Comment by bjornsing

> if you multiplied everything - hyperparameters, initialized weights, training data, etc in a network by 10^6 things will still work more or less the same since the upper range is hardly used (with the possible exception of some small number of special functions)

I doubt that very much. Thing is that inputs are multiplied with weights and added together in a neural network layer, and then the output becomes the input of the next layer in a cycle that can repeat up to a hundred times or more. When you get to the final output layer that 10^6 factor has been applied so many times that it has snowballed to a 10^600 factor.