Neural Synesthesia: GAN AI dreaming of music

Xander Steenbrugge shared his latest work on LinkedIn yesterday, and I was completely stunned! Xander had been working on, what he called, a “fun side-project”, but which was in my eyes, absolutely awesome. He had used two generative adversarial networks (GANs) to teach one another how to respond visually to changing audio cues. This resulted…

Object-Oriented Programming with Java

Now that I’m slowly familiarizing myself in the world of Python, I am much more often confronted with classes and object-oriented programming (OOP). While R has its own OOP paradigms (yes, multiple, obviously, it’s R after all), I have never experienced the need to create my own classes. However, in other languages, like Python, Ruby,…

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…

A tiny guide to Variable Fonts & Color Fonts

So, you’ve probably never heard of variable fonts. Well, I sure had not when I first came across the concept a week or so ago. And I was shocked. This looked so cool. As I adjusted the size of my browser, the text and images adjusted itself along. As I made my Chrome window bigger,…

Podcasts for Data Science Start-Ups

Christopher of Neurotroph.de compiled this short list of data science podcasts worth listening to. See Chris’ original article for more details on the podcasts, but the links below take you to them directly: Data Skeptic DataFramed Not So Standard Deviations Linear Digressions  Rework

Making GIFs with Processing

Processing is a flexible software sketchbook and a language for learning how to code within the context of the visual arts. It’s open-source, there are many online materials, and the language itself is very accessible. I recently stumbled upon 17-year-old Joseff Nic from Cardiff who has been making GIFs in Processing only since 2018, but…

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…

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away! ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps: building footprint massing program repartition…

Simulate Datasets with DrawData.xyz

Vincent Warmerdam shared his new tool to quickly simulate artificial datasets: http://www.drawdata.xyz. The drawdata.xyz tool allows you to easily create your own line- and scatter-plot with different groups of datapoints following specific x-y patterns. After drawing your data, you can just click to export your new dataset to csv or json format. x y 106.04…