Freelance Data Scientist in Ingolstadt, Germany, He/Him
Since August 2022, I've been working on a project called Aggregata, a blog where Jairus Joer and I write regularly about the web and AI.
I gave a seminar on the following subtopics of artificial intelligence:
- What is "A.I."?
- Introduction to Machine Learning Fundamentals
- Neural Networks and Deep Learning
- Minimization of the Loss Function
- Model Selection in Machine Learning
- Ethics in Machine Learning
- How do generative Models work?
- What could the future hold?
The seminar lasted approximately two hours.
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.
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.
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.
Event occurrence probabilities are often a complex problem to model. In this article, a basic method for this is presented: Logistic Regression.
This post is intended to introduce the reader to the basic topic of reinforcement learning. The introduction clarifies important terms and basic concepts that are used in Reinforcement Learning.
Semi supervised learning represents one of the four topics of machine learning. This post is intended to give an introduction to the topic.
Unsupervised Learning represents one of the four topics of Machine Learning. This post aims to provide an introduction to the topic.
Supervised Learning represents one of the four topics of Machine Learning. This post is intended to provide an introduction to the topic.
Neural networks are a hotly debated topic. But what are they and how exactly do they work? Some of these questions are answered here.
Machine Learning is taking an increasingly important part in our lives. This post will explain why is likely a relevant concept in the future.
The Deep Q network sometimes suffers from various problems. The problems are presented here and a solution for these problems is presented.
Deep Q-Networks sometimes need information from different time steps to converge quickly. The Deep Recurrent Q-Network represents one possibility for this.
In the Big Data business, (co-)rellations are often useful to make decisions. Here is one easy method to find such information: Linear Regression.
Artificial neural networks have achieved success in many fields. Here I present the first algorithm for training neural networks for reinforcement learning: the Deep Q-Network.