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turnip_burrito t1_j3bb4p2 wrote

Well, not graduate level. Junior level in college is sufficient for the most basic models. Like for feedforward neural networks, you just need to know chain rule from calculus and some summation series notation.

Apart from that, Bayesian probability, geometric series, convergence rules, constructing mathematical proofs, it's advanced but shouldn't take too long to pick up if taught correctly. But this stuff will take much longer (basically graduate level for meaningful work).

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BellyDancerUrgot t1_j3dxt4h wrote

Depends on what you define as basic. The post talks about novel approaches to AGI. Simple MLP is below basic in that regard. And even then I doubt most people learn about differentiation with respect to coordinate transformation in undergrad unless they do some highly specialized ML or math course.

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