DaDaDa2016: Daniel Weimer – Deep Convolutional Neural Networks in industrial applications


Deep Learning is a new (and at the same time old) paradigm in machine learning which allows to extract features directly from huge amounts of raw data with a minimum of human interaction. This talk gives an introduction about deep learning in general and focuses an important application of deep convolutional neural networks (CNN) in industrial settings: optical quality control.

Daniel studied electronic engineering with a focus on automation and robotics. After his master thesis in the field of autonomous driving and parallel computing he joined the Intelligent Production Systems group at University of Bremen where he worked in the field of smart production and logistics systems and headed the Computer Vision Lab. His PhD research in mathematics and computer science was awarded with a scholarship at Technion – Israel Institute of Technology in Haifa, Israel and compared deep learning and traditional machine learning in different applications. In his current position as a Project Manager and Data Scientist at Volkswagen Data:Lab he is responsible for Big Data, AI and digitalization projects along the whole automotive value chain.


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