Ethics and Bias in Machine Learning
As in many other areas, ethical issues are necessary for the safe and fair use of various products. In this article, we will look at ethical issues related to machine learning.
Robust clustering using k-Means
To identify similar groups in unknown data and reduce complexity, clustering can be used. Here we describe the k-means algorithm that can be used for clustering.
Dynamic imports with Alpine.js
Find out how you can implement dynamic imports with Alpine.js to speed up your website and optimize the user experience.
Astro - An introduction to your next project
Learn how to use Astro as a flexible base for your next project in this article that takes you from setup to deployment.
One year of Aggregata: A review and outlook
In October 2022 we published our first article and in October 2023 we will not only review one year of Aggregata but also give an outlook on future projects.
"How does A.I. learn?" - Impressions from our first seminar
Understanding Artificial Intelligence - In our latest article we give an insight into our first seminar and our experienc
On-Policy vs Off-Policy: What's the Difference?
The purpose of this article is to introduce the reader to the basic topic of reinforcement learning. The introduction concretizes important terms and basic concepts used there.
Regression using neural networks
There are a number of ways to solve a regression task. In this article we will describe how such a regression task can be solved using a neural network.
Classification using neural networks
There are several classical algorithms to perform a classification. Here I describe an implementation using neural networks and my experiences with it.
Q-Learning: The Basics of Reinforcement Learning
Q-Learning was one of the first practical reinforcement learning algorithms. This post introduces this algorithm.
Temporary and persistent state management with Alpine.js
In this article, you'll learn how to use Alpine.js to manage both temporary and persistent states, and how to use it to optimize your user experience.
Random Forests - An upgrade for Decision Trees
Decision trees alone are often not sufficient to perform a meaningful classification. Random forests represent an improvement of the decision trees. I am going to present this method in the following post.