Julia at NIPS and the Future of Machine Learning Tools – Julia Computing

We are excited to share several research papers on the Julia and Flux machine learning ecosystem, to be presented at the NIPS Systems for ML Workshop. Since initially proposing the need for a first-class language and ecosystem for machine learning (ML), we have made considerable progress, including the ability to take gradients of arbitrary computations by leveraging Julia's compiler, and compiling the resulting programs to specialized hardware such as Google's Tensor Processing Units. Here we talk about these papers and the projects that have brought these to life, namely: Flux.jl [paper], Zygote.jl Flux.jl is a library that gives a fresh take on machine learning as it exposes powerful tools to the user in a non-intrusive manner while remaining completely hackable, right to its core. "Careful design of the underlying automatic differentiation allows freely mixing mathematical expressions, built-in and custom layers and algorithms with control flow in one model. This makes Flux unusually easy to extend to new problems."