Umberto Michelucci is a cofounder and the chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. He’s an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book, Applied Deep Learning—A Case-Based Approach to Understanding Deep Neural Networks, was published by Springer in 2018. He’s working on his new book, Convolutional and Recurrent Neural Networks Theory and Applications. He’s very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences. Umberto studied physics and mathematics. Sharing is caring—for that, he is a lecturer at the ZHAW University of Applied Sciences for deep learning and neural networks theory and applications and at the HWZ University of Applied Science for big data analysis and statistics. He’s also responsible at Helsana Versicherung AG for research and collaborations with universities in the area of AI.