Feature engineering takes up a huge part in the work-life of a data scientist. Sometimes this doesn’t stop at features but also the target variable itself is transformed leading to all kinds of unexpected consequences. In this post, you will learn about common pitfalls, how a transformation can affect the error measure, the math behind it, and even how all this can be used to your advantage.

With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order …

Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In our work, we demonstrate an end-to-end trainable question answering (QA) system that allows a …

Recommender Systems support the decision making processes of customers with personalized suggestions. They are widely used and influence the daily life of almost everyone in different domains like e-commerce, social media, or entertainment. Quite often the dimension of time plays a dominant role in the generation of a relevant recommendation.

A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts …