The principle of reparameterization of network structure is not complicated, and to put it bluntly, it is like a mathematical associativity law.

It's almost the same as the law of addition, the law of multiplication, or something.

It belongs to the knowledge point of the second grade of primary school.

It's just that this time it's not a simple addition like 3+5, but a more complex neural network operator.

Which operators are linear and can be merged, how to merge is better, how to design branches, or slightly

The Godfather of Reborn AI 163. This is not math, it's in the hand, please wait a moment,

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