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A more recent framework in the [[Julia (programming language)|Julia]] programming language — [https://github.com/FluxML/Zygote.jl Zygote] — resolves the issues that earlier attempts faced by treating the language's syntax as the graph; the [[intermediate representation]] for arbitrary Julia code can then be differentiated directly, [[compiler optimization|optimized]], and compiled.<ref name="flux" /><ref>{{cite arxiv|last=Innes|first=Michael|date=2018-10-18|title=Don't Unroll Adjoint: Differentiating SSA-Form Programs|eprint=1810.07951|class=cs.PL}}</ref> An in-development differentiable programming language called [[Myia (programming language)|Myia]] also uses a similar approach.<ref name="myia1" />
==See also==
* [[Machine learning]]
==References==
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