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…

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!

ROC, AUC, precision, and recall visually explained

A receiver operating characteristic (ROC) curve displays how well a model can classify binary outcomes. An ROC curve is generated by plotting the false positive rate of a model against its true positive rate, for each possible cutoff value. Often, the area under the curve (AUC) is calculated and used as a metric showing how well…

3D visual representations of common neural network architectures

Came across this awesome Youtube video that blew my mind. Definitely a handy resource if you want to explain the inner workings of neural networks. Have a look! Reminded me of my other go-to resource when it comes to explaining neural nets, the playlists by 3Blue1Brown: I’ll surely add these to the other neural network…

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…

Putting R in Production, by Heather Nolis & Mark Sellors

It is often said that R is hard to put into production. Fortunately, there are numerous talks demonstrating the contrary. Here’s one by Heather Nolis, who productionizes R models at T-Mobile. Her teams even shares open-source version of some of their productionized Tensorflow models on github. Read more about that model here. There’s another great…

Northstar: The interactive, drag-and-drop data science platform by MIT

MIT researchers have spent years developing the new drag-and-drop analytics tools they call Northstar. Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze,…

Generalized Additive Models Tutorial in R, by Noam Ross

Generalized Additive Models — or GAMs in short — have been somewhat of a mystery to me. I’ve known about them, but didn’t know exactly what they did, or when they’re useful. That came to an end when I found out about this tutorial by Noam Ross. In this beautiful, online, interactive course, Noam allows…