14–17 Oct 2019
Umberto Michelucci

Umberto Michelucci
Google Machine learning Developer Expert, TOELT LLC

Website

Umberto Michelucci is a cofounder and the chief AI scientist at 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 (US) 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, and he’s working on a 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. At Helsana Versicherung AG, he’s also responsible for research and collaborations with universities in the area of AI.

Sessions

9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Hilton Meeting Room 3/4
Secondary topics:  Computer Vision
Umberto Michelucci (TOELT LLC)
Convolutional neural networks (CNNs) are the basis of many algorithms that deal with images, from image recognition and classification to object detection. Using practical examples, Umberto Michelucci walks you through developing convolutional neural networks, using pretrained networks, and even teaching a network to paint. TensorFlow or Keras will be used for all examples. Read more.

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