JuliaCon 2017 Highlights: Workshops, Keynotes, and Technical Advances on the West Coast
JuliaCon 2017, held on the US West Coast, featured an expanded schedule of workshops and talks aimed at the growing Julia programming community. The conference began with intensive workshops covering the DifferentialEquations ecosystem, Optim.jl for statistical learning minimization, and machine learning fundamentals implemented in pure Julia. Keynote speaker Fernando Perez discussed using Binder to share Jupyter notebooks, while Stefan Karpinski outlined improvements in the Pkg3 package manager. Technical sessions included Jameson Nash’s analysis of the Julia compiler, Tim Besard’s work on native GPU code-generation, and Mike Innes’ introduction to Flux, a functional machine learning library. Deniz Yuret presented KNet.jl, highlighting its use of dynamic computational graphs and automatic differentiation. Other notable presentations covered collision avoidance systems by Mykel Kochenderfer, a type system overhaul by Jeff Bezanson, and probabilistic programming with Turing.jl by Kai Xu. The event served as a platform for developers and researchers to share advancements in scientific computing, financial modeling, and deep learning within the Julia ecosystem.
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JuliaCon 2017 Highlights: Workshops, Keynotes, and Technical Advances on the West Coast
JuliaCon 2017, held on the US West Coast, featured an expanded schedule of workshops and talks aimed at the growing Julia programming community. The conference began with intensive workshops covering the DifferentialEquations ecosystem, Optim.jl for statistical learning minimization, and machine learning fundamentals implemented in pure Julia. Keynote speaker Fernando Perez discussed using Binder to share Jupyter notebooks, while Stefan Karpinski outlined improvements in the Pkg3 package manager. Technical sessions included Jameson Nash’s analysis of the Julia compiler, Tim Besard’s work on native GPU code-generation, and Mike Innes’ introduction to Flux, a functional machine learning library. Deniz Yuret presented KNet.jl, highlighting its use of dynamic computational graphs and automatic differentiation. Other notable presentations covered collision avoidance systems by Mykel Kochenderfer, a type system overhaul by Jeff Bezanson, and probabilistic programming with Turing.jl by Kai Xu. The event served as a platform for developers and researchers to share advancements in scientific computing, financial modeling, and deep learning within the Julia ecosystem.
JuliaLang - The Julia programming language