Learn how you can contribute to Fairlearn and how to contribute.

Scikit-learn is a popular machine learning library. It currently has over 200 estimators ready to use for a vast array of use cases. What if you are working on something special that still hasn't found its way into the library? Scikit-learn offers a way to write new compatible estimators, which can be seamlessly integrated with the rest of the library. We will look into what an estimator is, what API that scikit-learn estimators have, reasons why you would like to implement your own and an example of how to. We will end with real-world examples of how other OSS projects use this for their needs.

How would you model the mental hops that lead from one word to the next? And how about when instead of a word, the starting point are concepts grounded explicitly or implicitly in an image? These questions, and more, were the topic of my latest research project. Working to automatically generate image-term pairs for an image-grounded, collaborative Wordle game, I looked for combinations that spark the desired type of dialogue - illuminating the participants' decision-making. The project fits the broader efforts toward natural language explainability that Prof. Schlangen’s research group at the University of Potsdam is undertaking. We will look at the method I developed from an engineering perspective, going over all the NLP concepts composing it, and touch upon a bit of linguistics theory too. Level: Beginner to the domain (already familiar with Python)