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Flux: Elegant Machine Learning with Julia

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This talk will introduce Flux, a library for machine learning (ML). Flux is written in Julia, a language designed for high-performance numerical computing, in around 1,000 lines of straightforward code. This talk will explain how to build Flux, starting with the basics of Julia and how it compiles high-level mathematical programs to tight machine code, and how the compiler can be extended to support ML.

Deep learning models have become complex, and increasingly look like “differentiable algorithms”; through this lens, ML systems problems become language and compiler problems. We illustrate how augmenting a numerical language with compiler-level automatic differentiation enables an unusually flexible and hackable approach to ML, one which provides an elegant define-by-run style while supporting new kinds of models, cutting edge hardware and state-of-the-art performance.

This talk is part of the Microsoft Research Cambridge, public talks series.

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