Practical Guide to Transformer Fine-Tuning
Transformers have taken an ever-more-important place in our lifes. In this article, we focus on how we can finetune such a model for our needs.
Text Classification with the Naive Bayes Text Classifier
The Naive Bayes classifier is a simple and efficient algorithm for classification tasks that assumes independence of the features. This post aims to introduce this algorithm to the reader.
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.
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.
Classification via k-nearest-neighbor classifier
Classification can be done in many different ways and with many different algorithms. Today I will introduce the K-Nearest-Neighbour classifier.
Decision Trees - Graphical Explainable AI
Often machine learning models are complex and difficult to understand. This article describes a method that visualizes clear structures and decision criteria: Decision Trees.
Logistic Regression - Prediction of Event Occurrences
Event occurrence probabilities are often a complex problem to model. In this article, a basic method for this is presented: Logistic Regression.
An Introduction to Supervised Learning
Supervised Learning represents one of the four topics of Machine Learning. This post is intended to provide an introduction to the topic.
Linear Regression
In the Big Data business, (co-)rellations are often useful to make decisions. Here is one easy method to find such information: Linear Regression.