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2022-09-01.log
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<rekado>I recently watched a video on micrograd as a way to learn backpropagation and gradient descent in neural networks. <rekado>it’s written in Python, so I thought it would be a good idea to translate it to Scheme. <rekado>all the way I was annoyed by the OOP style that underpins graph representations and algorithms <rekado>I’d be happy to see more Scheme in the space of neural networks. <rekado>we have aiscm, but I’d be happier if it didn’t tied me to tensorflow. <civodul><context> is used in a way similar to the "zipper" structure, right? <rekado>match-context works like a zipper <rekado>it matches on a graph and returns the context + the remaining graph (without that context). <rekado>match-context and graph-cons are a bit expensive because they need to protect the ordering invariant that a context can only refer to nodes that already exist in the graph <rekado>now that I have this little graph library I see that it suggests being used in a monad <rekado>micrograd is a little expression language that generates a graph as you apply operations to values; each resulting value keeps references to the terms that went into the operation. <rekado>when working with contexts, though, it seems that we should thread the graph through operations —> monad <civodul>fun, i'm offered to participate in an event 3 weeks from now at the other end of the world <zimoun>civodul: ahah! Is it Guix 10 Years ? Because Paris is not the other end of the world; I mean from your place. ;-) <nckx>The (populated) antipode of Paris is Waitangi, Chatham Islands, New Zealand. <nckx>I guess they have a LUG. <rekado>the (chartered) ship has already sailed