An introduction to Julia for machine learning

Abstract: In this workshop, we assume no prior exposure to Julia, and will show you why Julia is a fantastic language for machine learning. It should be accessible useful to data scientists and engineers of all levels, as well as anyone else with technical computing needs and an interest in machine learning. Our goal is that attendees will leave the workshop with an understanding of how easy it is to start programming in Julia, what makes Julia special, and how using Julia for machine learning applications will improve your workflow as a data scientist.

All workshop materials will be provided on juliabox.com so that attendees can operate in a common environment and code along with the instructor.

The first thirty minutes of the workshop will cover language basics and show you how easy it is to pick up Julia’s high-level syntax. To get you up and running with Julia, we will go over syntax for function declarations, loops, conditionals, and linear algebra operations.

The second thirty minutes of the workshop will highlight Julia’s performance. Attendees will learn how to benchmark, see first-hand how quickly Julia code runs compared to C and Python, and learn to take advantage of special features from Julia’s linear algebra infrastructure. Finally, attendees will come to understand how multiple dispatch, a key feature of Julia’s design, helps to make Julia both high-level and performant.

In the last 45 minutes, we will cover special tools for data science and machine learning, where you will see how easy it is to recognize letters in your own handwriting using Flux.

Bio: Jane Herriman is Director of Diversity and Outreach at Julia Computing and a PhD student at Caltech. She is a Julia, dance, and strength training enthusiast and is excited for the opportunity to teach you Julia.

Open Data Science Conference