I’m a software engineer from Germany. My focus is to broadcast the fascination behind machine learning to anyone and growing my own skills in the process.
Optimizing a (complex) function can be a difficult task. Here I present a library which I use and which I think is a good way to solve such tasks. In addition, I will show corresponding tasks and an implementation of a comparatively simple optimization task as a usage example.
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.
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.
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.
Understanding Artificial Intelligence - In our latest article we give an insight into our first seminar and our experienc
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.
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.
There are several classical algorithms to perform a classification. Here I describe an implementation using neural networks and my experiences with it.
Q-Learning was one of the first practical reinforcement learning algorithms. This post introduces this algorithm.
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.
Classification can be done in many different ways and with many different algorithms. Today I will introduce the K-Nearest-Neighbour classifier.
Often machine learning models are complex and difficult to understand. This article describes a method that visualizes clear structures and decision criteria: Decision Trees.
