In standard Machine Learning, we use Backpropagation. It’s an omniscient global coach. When the network makes a mistake, we calculate a global error gradient and pass it backward, updating every single weight perfectly using calculus.
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I think at the time, part of it was we were expecting the compiler to be smart enough to see where the range objects were used and then do, like, if you iterated over a range, it would be very smart about, you know, loop unrolling and things like that.