Posts
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Tech Tips #0
I’m launching a new category dedicated to small tech tips, where I’ll document the challenges and solutions I encounter during my PhD journey, as well as in my broader research and technology-related endeavors.
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Academic Resources
Grad students are often confronted with a great task in order to finish their thesis. From writing our thesis, keeping up with the state-of-the-art of our areas. Publishing quality content, with text, images and tables with industry-grade quality while managing to balance all of this into tight schedules is hard work. This is especially important at the PhD level where it takes several years of this way of life.
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Regression Tutorial with Julia lang
Hi, in my last post, I showed how Julia can be used to perform a classification task. In that case, we classified patients into two categories, so it was a classification, which is a method for predicting a, you guessed it, categorical outcome. But now, we are going to do a regression, where the outcome variable is continuous.
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Introduction to Machine Learning with Julia
The Julia language was originally released in 2012 by Alan Edelman, Stefan Karpinski, Jeff Bezanson, and Viral Shah. Its popularity has been increasing exponentially in the last few years and its speed and community have been key. Furthermore, and taking the words of Ben Lauwens in its book Think Julia the reasons for picking up Julia are: Julia is developed as a high-performance programming language.
- Julia uses multiple dispatches, which allows the programmer to choose from different programming patterns adapted to the application.
- Julia is a dynamically typed language that can easily be used interactively.
- Julia has a nice high-level syntax that is easy to learn.
- Julia is an optionally typed programming language whose (user-defined) data types make the code clearer and more robust.