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<korvo>
https://github.com/statusfailed/catgrad This is quite cool: gradient descent defined in terms of a type signature using hypergraphs and semirings, and homomorphically compiled to multiple backends using the standard compiling-to-categories lore.
<korvo>
I might add an RPython backend once they mature a bit more and ossify some of the interfaces; it would emit code similar to the Python 3 backend, but using some sort of RPython matrix-multiplication library.
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<korvo>
I don't want this for techbro BS, but for the humbler task of learning the parameters to some temperature sensors. I know what the sensors are like at compile time, so I should be able to compile a simple gradient-descent system based on the sensor layout.
<korvo>
And then I won't have to do the cross algorithm for gradient-free optimizing, which is not especially efficient in high dimensions.
<korvo>
...Okay, I *am* thinking about building a Gödel machine, but it would just be for automatically proving Metamath theorems and maybe automatically improving speedruns.