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Interesting library & idea, almost like its own programming paradigm when you abstract away all the specificity for building software or running ETL jobs or whatever.

But this is a completely different kind of graph. The graphs being discussed here are differentiable DAGs of mathematical computations.

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Is it that different? https://github.com/timkpaine/tributary/blob/main/docs/exampl...

Now that is definitely interesting. And you have some notion of "differentiability" for all of your various sources, sinks, and transforms?

That said, Tensorflow and Pytorch are both very much general purpose numerical computing libraries. You don't have to use them for neural networks.

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