News

Julia Debugging Basics by Gregory Farage

Written by Tristan Hayes

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BERDJulia

This is a practical how-to guide on best practices for debugging code in Julia using the Gallium package. We explore two methods: REPL and Juno-Atom. Installation To use the debugger Gallium in Julia 0.6+, the following packages should be installed: Gallium and ASTInterpreter2. julia> Pkg.add(“Gallium”) julia> Pkg.clone(“https://github.com/Keno/DebuggerFramework.jl”) julia> Pkg.clone(“https://github.com/Keno/ASTInterpreter2.jl”) There are two possible ways to debug with Gallium,… Read More

A Quick Recommendation on SAS Setup at UT by Fridtjof Thomas

Written by Tristan Hayes

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BERDSAS

This is a quick guide on the recommended SAS setup for UT users. This is excerpted from a longer guide on software setup and windows 10. SAS 9.4 TSIM3 Large (16GB) download from UT Knoxville This download will take several hours due to its size (zipped file 16GB).  You may want to ask the Biostatistics… Read More

Real Close to the Machine: Floating Point in D | Using Floating Point Without Losing Your Sanity by Dan Clugston [outside article & video]

Written by Tristan Hayes

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BERDComputation

An entertaining look at floating point computation and absurdities that you might encounter if you are not careful. The video is entertaining; the details are in the article. Computers were originally conceived as devices for performing mathematics. The earliest computers spent most of their time solving equations. Although the engineering and scientific community now forms… Read More

Missing values in Julia by Milan Bouchet-Valat [outside article]

Written by Tristan Hayes

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BERDComputationJulia

Starting from Julia 0.7, missing values are represented using the new missing object. Resulting from intense design discussions, experimentations and language improvements developed over several years, it is the heir of the NA value implemented in the DataArrays package, which used to be the standard way of representing missing data in Julia.

Gradient Descent: The mother of all algorithms? by Aleksander Mądry [outside lecture]

Written by Tristan Hayes

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BERDComputation

More than half a century of research in theoretical computer science has brought us a great wealth of advanced algorithmic techniques. These techniques can be combined in a variety of ways to provide us with sophisticated, often beautifully elegant algorithms. This diversity of methods is truly stimulating and intellectually satisfying. But is it also necessary?