Posts

Principal component analysis (PCA)

After a longer break I continued working on my machine learning basics repository which implements fundamental machine learning algorithms in plain Python. This time, I took a detailed look at principal component analysis (PCA). The blog post below contains the same content as the original notebook. You can run the notebook directly in your Browser using Binder. 1. What is PCA? In simple terms, principal component analysis (PCA) is a technique to perform dimensionality reduction.

August 11, 2022 Read

Support vector machines

I posted another notebook in my machine learning basics repository. This time, I took a detailed look at support vector machines. The blog post below contains the same content as the original notebook. You can run the notebook directly in your Browser using Binder. 1. What are support vector machines? Support vector machines (short: SVMs) are supervised machine learning models. They are the most prominent member of the class of kernel methods.

April 13, 2021 Read

Personal reading list of non-fiction books

Books I am currently reading Atomic Habits by James Clear Link to book Books I have read A Promised Land by Barack Obama (Link to book) The Alchemy of Air by Thomas Hager (Link to book) Superintelligence: Paths, Dangers, Strategies by Nick Bostrom (Link to book) Beethoven, A Life by Jan Caeyers (Link to book) Deep Work: Rules for Focused Success in a Distracted World by Cal Newport (Link to book)

April 6, 2021 Read

Bayesian linear regression

I finally found time to continue working on my machine learning basics repository which implements fundamental machine learning algorithms in plain Python. Especially, I took a detailed look at Bayesian linear regression. The blog post below contains the same content as the original notebook. You can run the notebook directly in your Browser using Binder. 1. What is Bayesian linear regression (BLR)? Bayesian linear regression is the Bayesian interpretation of linear regression.

February 20, 2021 Read

Bayesian linear regression 2

February 20, 2021 Read

Deep Work

I recently finished reading the book Deep Work by Cal Newport. For years, I have been interested in the question of how we can improve our learning and work habits. There is a ton of research on this topic and many great resources (like, for example, the podcast The Learning Scientists). I don’t want to use this blog post to talk about all the different things I have learned and tried in the past years.

December 18, 2020 Read

Mocking in Python

Topics: Mocking in Python Today, I want to talk about mocking. I became interested in this topic a few months back when I started to work in a data engineering project at work. In this project we have a lot of tests that relies on mocking to test code with external dependencies. If you don’t like reading long blog posts, consider listening to one of the podcast episodes I did on this topic: there is one at Talk Python to Me and the other at Test and Code.

October 11, 2020 Read

Variational Inference

Introduction Variational inference is an important topic that is widely used in machine learning. For example, it’s the basis for variational autoencoders. Also Bayesian learning often makes use variational of inference. To understand what variational inference is, how it works and why it’s useful we will go through each point step by step. What are latent variables? A latent variable is the opposite of an observed variable. This means that a latent variable is not directly observed but inferred from other variables which are observed.

February 23, 2019 Read

Kullback-Leibler Divergence

One of the points on my long ‘stuff-you-have-to-look-at’ list is the Kullback-Leibler divergence. I finally took the time to take a detailed look at this topic. Definition The KL-divergence is a measure of how similar (or different) two probablity distributions are. When having a discrete probability distribution $P$ and another probability distribution $Q$ the KL-divergence for a set of points $X$ is defined as: $$D_{KL}(P ,|| ,Q) = \sum_{x \in X} P(x) \log \big( \frac{P(x)}{Q(x)} \big)$$

February 2, 2019 Read

An introduction to Docker

Topics: Docker I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! After writing a post on virtual machines and another about containers this one is finally about Docker!

November 10, 2018 Read

An introduction to containers

Topics: Containers I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! With the goal of eventually reaching the topic of Docker, the first post introduced virtual machines.

October 7, 2018 Read

An introduction to virtual machines

Topics: Virtual machines I have been wanting to learn more about Docker for months. However, when starting to read Dockers get started page I quickly had to find out that I’m lacking knowledge in too many other concepts. Since my background isn’t computer science I often run across this problem. Luckily, this allows me to constantly learn new things! With the goal of eventually reaching the topic of Docker, this post will introduce virtual machines, while the next one will be about containers.

September 29, 2018 Read