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

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

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,

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