Differentiable programming for gradient-based machine learning

I don't want to go into discussing clients outside of the Swift open source project here, but for autodiff itself to be eligible for Swift Evolution, we do not need to implement transposition or forward-mode differentiation that was described in the manifesto. The initial implementation should be in line with the proposal in this thread, that is, reverse-mode differentiation (based on VJPs) that we have already implemented today.

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