simply it has the same prediction exactly as that of a kernel method the kernel is called the neural tangent kernel and your generalization bound will look exactly like rate regression will be something like like label Y transpose kernel inverse y / n whole thing under square root so that's some generalization bound you can extract from this style of analysis so I have no I I don't think it's very tight in fact I'll talk about later okay so let's look a little bit more at generalization error what's happening a very standard