Julia comes ready with Flux, a state-of-the-art framework for machine learning and AI.

As machine learning systems become more complex, researchers increasingly need differentiable languages in which they can simply write their algorithms as code. Julia’s mathematical syntax makes it an ideal way to express algorithms just as they are written in papers, while Flux turns code into trainable models with automatic differentiation, GPU acceleration and support for terabytes of data via JuliaDB.

Flux provides a highly intuitive, layer-stacking-based interface for simple models, yet you can easily write your own mathematics for more advanced ones such as variational auto-encoders. Despite how easy it is to use, Flux is the most flexible ML framework on Earth; one can easily integrate it with other Julia libraries like physics simulators or differential equations solvers, and even drop down to writing a quick CUDA kernel via CUDAnative; all from one language, or even from a single script!

Julia also has best-in-class support for other modern machine learning frameworks such as TensorFlow and MXNet, making it easy to adapt to existing workflows.

Mike Innes - Machine Learning with Julia (Flux)


At JuliaCon 2017, our very own Mike Innes gives a brief introduction to Flux.jl, a Julia package that expands Julia’s flexibility in machine learning use cases. He further goes on to show how it provides lightweight abstractions on top of Julia’s native GPU and Automatic Differentiation support, while remaining fully hackable.


Jonathan Malmaud - Modern Machine Learning in Julia with TensorFlow.jl

Another talk from JuliaCon 2017, this time from Jonathan Malmaud, an MIT researcher working on cutting edge machine learning technologies, demonstrating how Julia’s interfaces to popular Machine Learning frameworks are succinct and seamless to use, illustrated with the example of Julia’s TensorFlow.jl.

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Tangent Works Source: JuliaComputing.com

Tangent Works uses Julia to build a comprehensive analytics solution that blurs the barrier between prototyping done by data scientists and production development done by developers.

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