Python tidbits - The mysterious behaviour of int

When working with datatypes in Python I keep getting surprised by their intricate dynamic nature. When working with integer values in other programming languages we often have to declare the precision of the types that we are using. In the case of integers, we have a wide variety of ranges... [Read More]

Graph Convolutional Neural Networks

As more and more businesses strive toward becoming data-driven, the use of graph methods for storing relational data has been on the rise ( [1], [2], [3]). Along with these graph databases comes more opportunities for analysing the data, including the use of predictive machine learning models on graphs. [Read More]

Doubt - Bringing back uncertainty to ML

I have previously been exploring uncertainty measures that we can build into our machine learning models, making it easier to see whether a concrete prediction can be trusted or not. This involved confidence intervals for datasets and prediction intervals for models; see the previous posts in this series for a... [Read More]


Deep learning has almost exclusively been working with simple objects: images and text. By simple I am here referring to the graphical structure of these objects, where a word is a linear sequence of letters, a document is a linear sequence of words, and an image is a rectangular grid... [Read More]

The PageRank Algorithm

I’ve recently started working with graph structures in the context of machine learning, and have found that I’ve opened what seems to be a reverse Pandora’s box, full of neat algorithms that can pull out a lot of insights from graph structures. As a way of cementing my knowledge and... [Read More]