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Fashionable Modelling with Flux
Machine learning as a discipline has seen an incredible surge of interest in
recent years due in large part to a perfect storm of new theory, superior
tooling, renewed interest in its capabilities. We present in this paper a
framework named Flux that shows how further refinement of the core ideas of
machine learning, built upon the foundation of the Julia programming language,
can yield an environment that is simple, easily modifiable, and performant. We
detail the fundamental principles of Flux as a framework for differentiable
programming, give examples of models that are implemented within Flux to
display many of the language and framework-level features that contribute to
its ease of use and high productivity, display internal compiler techniques
used to enable the acceleration and performance that lies at the heart of Flux,
and finally give an overview of the larger ecosystem that Flux fits inside of