# Tag: machine learning

## Inverse Problem (Part 3)

In the two previous posts I wrote about inverse problems (part 1 and part 2). For a proper introduction into inverse problems I refer to these posts. In my last post about inverse problems, I have showed you how to describe a prediction (classification problem) in terms of an inverse problem and how to solve

## Inverse Problem (Part 2)

In the last post I have written about inverse problems. A simplified toy example was presented, which showed you how to translate this problem into an optimization problem. Optimization problems can be solved with multiple algorithms, e.g. gradient descent or evolutionary algorithms. This article presents a more sophisticated inverse problem. We want to classify images

## Inverse Problem (Part 1)

The process of calculating the causal factors from an observation is called inverse problem. An inverse problem is much harder to solve than the corresponding forward counterpart, which is calculating the observation from the causal factors. Many problems in science and math are inverse problems. They can be found in optics, radar, acoustics, communication theory,

## How to use pytest in automatic code generation

This notebook shows you how to write a plugin for Pytest. This allows us to use the pytest functionaltiy, e.g. test-discovery, in our automatic programming scenario. Another advantage is that many developers are already familar with pytest. Therefor, it would be much easier for those people to apply this development technique. We have seen in

## Archetypal Analysis

Recently I have read about Archetypal Analysis. It is an unsupervised learning algorithm similar to clustering analysis and dimensionality reduction. It has been introduced by Adele Cutler and Leo Breiman in 1994. In my opinion this idea doesn’t get enough attention, although there are good reasons to learn about it. The Archetypal Analysis has nice