Julia-Sydney-Talks

Date	Presenter	Topic
2018 Apr 19	Rowan	TBA
2018 Jan 18	Colin Bowers	Why I Chose Julia, or, an Exercise in the Statistical Bootstrap In this presentation, I discuss the reasons that in 2014, I chose to convert my entire workflow to Julia. The reasons are explained using my registered Julia package, DependentBootstrap, as a contextual example.
2017 Dec 14	Dai, Zhuo Jia	Can Julia be the fastest in-memory data manipulation language too? We all know Julia is meant to be fast. But there is an important caveat - it is fast at compute tasks but is it fast at data tasks where R's data.table is king? Can it be faster than data.table? This talk will offer an "into the future" look at Julia's data ecosystem.
2017 Nov 17	John Wormell	Numerical computing with functions in Julia While all we classically expect of a function in programming is that it produces outputs from inputs, knowledge of the function as a whole is really required for many operations: these include calculus operations, differential equation solving, root-finding and sampling from a distribution. This talk will introduce some Julia packages that implement various kinds of natural, highly efficient "whole-function" manipulation. It will focus on ForwardDiff.jl, which performs efficient automatic differentiation, and ApproxFun, which represents functions very accurately as a sum of Chebyshev polynomials and then solves many problems using numerical linear algebra. We will discuss in particular how these packages make use of Julia's capabilities, including multiple dispatch and the parametric type system. https://github.com/Julia-Sydney/Julia-Sydney-Talks/tree/master/20171116
2017 Oct 19	Joel Mason	Introduction to Julia The Julia programming language ( https://julialang.org/ ) is a high level language specialised for numerical computing, originally developed at MIT, that was first publicly released in 2012. Julia code looks a bit like Python or Matlab, but is designed from the ground up with a focus on high performance (approaching that of C). This talk will give an introduction to Julia, and discuss features of the language that make it well suited for machine learning, data science, and scientific computing in general. It will also discuss the challenges and opportunities of working with a relatively young language, with a smaller software library ecosystem/open source community than the likes of R, Python/NumPy/SciPy, and Matlab.
2017 Aug 17	Julia Lee	How to get started with Julia on Azure