Abstract
We provide a general introduction to convolutional neural networks (CNNs) in this tutorial. CNNs are particularly well suited to find common spatial structure in images or time series. As an insurance related example for life & health insurance we illustrate how to use a CNN to detect anomalies in mortality rates taken from the Human Mortality Database (HMD); the anomalies are caused by migration between countries and other errors. As a second example, we study a CNN to classify images of handwritten digits taken from one of the most widely used benchmark datasets, the Modified National Institute of Standards and Technology (MNIST) dataset. Our aim is to explore and discuss the building blocks and the properties of these CNNs, and we showcase their use.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
