Welcome to Lemonfold.io#

Welcome to my personal website and blog. The name “lemonfold” reflects my fascination for geometric methods in machine learning and robotics especially related to folding and unfolding. It is a verbatim and deliberately inaccurate translation for the German name of Gonepteryx rhamni—a beautiful yellow/lime green butterfly (which happens to be my favorite color). The technology behind this page has been inspired (read: shamelessly copied) from Chris Holdgraf’s website so kudos to him!

On this site, you can find out more about me here and read my blog. I love teaching and explaining so I hope you find my articles interesting and you enjoy reading. Feel free to leave some feedback. You will need a GitHub account and adjust the privacy level accordingly (the cookie symbol at the top) to do so.

Gonepteryx rhamni

This is how Gonepteryx rhamni looks like.#

Recent posts#

See the blog archives for a more complete list.

  • 2023-05-26 - AI/ML Essentials part III: Gaussian Process Regression

    In this article in the AI/ML Essentials series, we will learn about yet another machine learning model—Gaussian Process Regression. In the previous articles, we encountered instances of unsupervised learning (for clustering and density estimation). The last issue presented a simple taxonomy of machine learning, which guides us through the series:

  • 2023-01-15 - AI/ML Essentials Part 2: Cluster analysis with (Gaussian) Mixture Models

    This is the second part of the series “AI/ML Essentials”, which intends to be a gentle introduction to the topic using mostly layman’s terms and as little mathematics as possible–you don’t need an engineering degree to see the beauty within this fascinating field.

  • 2022-10-29 - AI/ML Essentials Part1: Self-Organizing Maps

    This article is the start of a series that introduces different models and algorithms to beginners

  • 2022-09-03 - Fully-typed Python decorator with optional arguments

    This is my very first blog post! In this short post, I will show the blueprint for a fully-typed Python decorator that optionally accepts parameters.