Drost

Breakthroughs on the way towards GPT — explained for non-expertsThe overwhelming attention large language models like GPT get in media today creates the impression of an ongoing revolution we all are in the middle of. However, even a revolution builds on the successes of its predecessors, and GPT is the result of decades of research.In this post, I want to give an overview of some of the major steps in research in the realm of language models, that eventually led to the large language models we have today. I will briefly…

Other criteria you should consider during model selectionThe difference between model selection and donut selection: In model selection, you can only choose one. Photo by ELISA KERSCHBAUMER on UnsplashAs a data scientist or machine learning engineer, you spend much of your time improving a model’s performance by creating new features, comparing different types of models, trying out new model architectures, and much more. In the end, it’s the score on the test set that counts, so that is what you focus on when deciding on…

Why causing your model to fail is the best thing you can doPhoto by Michael Dziedzic on UnsplashWhen developing a new model or algorithm, it is tempting to test it over and over again with similar examples that all work perfectly. While this may be fun, it doesn’t really help you in understanding and improving your model. You learn from errors, so cause your model to fail!Imagine your data science teammate comes to you and tells you about the new model they just trained. It is so awesome, it can classify all kinds of baby…