The Mental Game of Python, by Raymond Hettinger

YouTube recommended I’d watch this recorded presentation by Raymond Hettinger at PyBay2019 last October. Quite a long presentation for what I’d normally watch, but what an eye-openers it contains! Raymond Hettinger is a Python core developer and in this video he presents 10 programming strategies in these 60 minutes, all using live examples. Some are…

An Introduction to Docker for R Users, by Colin Fay

In this awesome 8-minute read, R-progidy Colin Fay explains in laymen’s terms what Docker images, Docker containers, and Volumes are; what Rocker is; and how to set up a Docker container with an R image and run code on it: On your machine, you’re going to need two things: images, and containers. Images are the definition…

Overviews of Graph Classification and Network Clustering methods

Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups: Factorization Spectral and Statistical Fingerprints Deep Learning Graph Kernels Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers…

17 Principles of (Unix) Software Design

I came across this 1999-2003 e-book by Eric Raymond, on the Art of Unix Programming. It contains several relevant overviews of the basic principles behind the Unix philosophy, which are probably useful for anybody working in hardware, software, or other algoritmic design. First up, is a great list of 17 design rules, explained in more…

Data Visualization Style Guide Repositories

Amy Cesal put together (1) this great overview of style guides for data visualization practice. Moreover, in the original tweet, Amy refers to other great repositories such as (2) this PolicyViz one and (3) this humongous one by Adele. Amy’s list includes many references to the best practices used by some of the leading data…

Causal Random Forests, by Mark White

I stumbled accros this incredibly interesting read by Mark White, who discusses the (academic) theory behind, inner workings, and example (R) applications of causal random forests: EXPLICITLY OPTIMIZING ON CAUSAL EFFECTS VIA THE CAUSAL RANDOM FOREST: A PRACTICAL INTRODUCTION AND TUTORIAL (By Mark White) These so-called “honest” forests seem a great technique to identify opportunities…

Tidy Machine Learning with R’s purrr and tidyr

Jared Wilber posted this great walkthrough where he codes a simple R data pipeline using purrr and tidyr to train a large variety of models and methods on the same base data, all in a non-repetitive, reproducible, clean, and thus tidy fashion. Really impressive workflow!

Learn Git Branching: An Interactive Tutorial

Peter Cottle built this great interactive Git tutorial that teaches you all vital branching skills right in your browser. It’s interactive, beautiful, and very informative, introducing every concept and Git command in a step-by-step fashion. Have a look yourself: https://learngitbranching.js.org/ Here’s the associated GitHub repository for those interested in forking. The tutorial includes many levels…

Artificial Stupidity – by Vincent Warmerdam @PyData 2019 London

PyData is famous for it’s great talks on machine learning topics. This 2019 London edition, Vincent Warmerdam again managed to give a super inspiring presentation. This year he covers what he dubs Artificial Stupidity™. You should definitely watch the talk, which includes some great visual aids, but here are my main takeaways: Vincent speaks of…