Helpful resources for A/B testing

Brandon Rohrer — (former) data scientist at Microsoft, iRobot, and Facebook — asked his network on Twitter and LinkedIn to share their favorite resources on A/B testing. It produced a nice list, which I summarized below. The order is somewhat arbitrary, and somewhat based on my personal appreciation of the resources. Course: A/B-testing by Google…

Visualizing the inner workings of the k-means clustering algorithm

Originally, I wrote this blog to share this interactive visualization of the k-means algorithm (wiki) which I was all enthusiastic about. However, then I imagined that not everybody may be familiar with k-means, hence, I wrote the whole blog below.  Next thing I know, u/dashee87 on r/datascience points me to these two other blogs that had already…

Evolving Floorplans – by Joel Simon

Joel Simon is the genius behind an experimental project exploring optimized school blueprints. Joel used graph-contraction and ant-colony pathing algorithms as growth processes, which could generate elementary school designs optimized for all kinds of characteristics: walking time, hallway usage, outdoor views, and escape routes just to name a few.   Definitely check out the original write-up if you…

Machine Learning & Deep Learning book

The Deep Learning textbook helps students and practitioners enter the field of machine learning in general and deep learning in particular. Its online version is available online for free whereas a hardcover copy can be ordered here on Amazon. You can click on the topics below to be redirected to the book chapter: Part I: Applied Math…