Convolutional neural network case studies: (1) anomalies in mortality rates (2) image recognition
24 Pages Posted:
Date Written: July 19, 2020
We provide a general introduction to convolutional neural networks (CNNs) in this tutorial.
CNNs are particularly well suited to nd 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 Modied 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.
Keywords: Convolutional neural network, CNN, regression, classication, mortality rates
JEL Classification: G22
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