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In machine learning (ML), researchers and engineers seem to be at odds.System implementers would prefer models to be declarative, with detailed typeinformation and semantic restrictions that allow models to be optimised,rearranged and parallelised. Yet practitioners show an overwhelming preferencefor dynamic, imperative languages with mutable state, and much engineeringeffort is spent bridging the resulting semantic divide. Is there a fundamentalconflict? This article explores why imperative and functional styles are used,and how future language designs might get the best of both worlds.

Functional or imperative? On pleasant semantics for differentiable programming languages