## 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…

## 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…

## Dynamic Programming MIT Course

Cover image by xkcd Over the last months I’ve been working my way through Project Euler in my spare time. I wanted to learn Python programming, and what better way than solving mini-problems and -projects?! Well, Project Euler got a ton of these, listed in increasing order of difficulty. It starts out simple: to solve…

## 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…

## 2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors. Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public. Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking. The author of the winning…

## Data Engineering Reading List, by Mapflat

Lars Albertsson, former software engineer at Spotify and Google and currently freelance data engineer via mapflat, maintains this list of data engineering resources. It includes many links to videos and courses about data pipelines, batch processing, Kafka, NoSQL, Clojure, Scala, Parquet, Luigi, Storm, Spark, Hadoop, Cassandra, and other tools I am not too familiar with….

## Understanding Data Distributions

Having trouble understanding how to interpret distribution plots? Or struggling with Q-Q plots? Sven Halvorson penned down a visual tutorial explaining distributions using visualisations of their quantiles. Because each slice of the distribution is 5% of the total area and the height of the graph is changing, the slices have different widths. It’s like we’re…

## 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…

## 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…