Deep Learning for Predictive Engineering

This course offers a hands-on, code-first introduction to Deep Learning for predictive engineering applications encompassing the latest advances in deep neural networks and big data analytics in the field of reliability, maintenance, and risk. By examining real-life case studies, students will be exposed to the main concepts, techniques, and challenges required to conceptualize, train, and deploy deep learning based predictive engineering solutions. The students will become familiar with a structured end-to-end real-world approach for such an objective which involves the following steps: (1) capture various types of data from information systems (SQL, SAP); (ii) preprocess the available data and prepare it with the specific format required by the target predictive solution; (iii) implement, train, and assess the deep learning solution; (iv) optimization of the predictive solution so to achieve predefined performance targets; (v) deployment of the developed predictive solution via an easy to use cloud computing framework.