GradIEEEnt half decent: The hidden power of imprecise lines

What people said

jcparkyn · 2024-05 · Recommendation

A fun example of that (making a neural network using floating point error as a source of non-linearity): [VIDEO]

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jcparkyn · 2024-10 · Reference

> squash an output into a range This isn't the primary purpose of the activation function, and in fact it's not even necessary. For example see ReLU (probably the most common activation function), leaky ReLU, or for a sillier example: [VIDEO]

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Uehreka · 2023-10 · Reference

I don’t get what people are trying to say when they say these kinds of things about AI. That human-level writing is as simple as a linear regression? That we could’ve had computer programs capable of human-level writing decades ago? Have they not used these AIs enough to see how powerful they are? Are they seeing the bad outits and thinking that AIs are always doing that poorly? Like seriously, if you’re telling me that it was obvious that a “linear regression” could pass the LSAT I’ve got a macvlan to sell you. Edit: they’re also literally not linear regression! [VIDEO]

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