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…

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

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…

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…