preface

More than a decade ago, while completing my BS and MS, I studied bioinformatics, the intersection of computer science and biology. We used software to solve problems related to living organisms, working with algorithms for sequence comparison, clustering, support vector machines, decision trees, and other methods that were close to what we would now call data science. During that time, I also realized something important: I loved coding. Software was going to be my craft.

A few years later, while working at SafeDK, later acquired by AppLovin, I found my way back to those roots. We needed to build an image classifier, and I was eager to take it on. My CEO decided to bring in an experienced advisor instead, which was probably the right decision. I followed the advisor closely, joined meetings, and took notes. One thing stayed with me: whenever he mentioned precision and recall, everyone in the room nodded in silence. The concepts clearly mattered, but they also seemed mysterious. That moment sparked my motivation to understand machine learning more deeply and to make these ideas clearer to others.