Tutorial Abstract

Tutorial (T2) Kai Lars Polsterer (HITS gGmbH)

Machine learning

A hands on introduction to neural networks. From simple neurons to deep neural architectures. The recent popularity of deep and complex artificial neural networks lead to an intensive usage of this technology in many applications. To understand the shortcomings and problems as well as the advantages and strengths of neural networks, an overview is required. Providing an compact overview of neural networks is the aim of this tutorial.

This tutorial is an extension to the activities that happened during the last ADASS meetings, which aimed for providing an overview of different aspects of machine learning. The participant will learn how artificial neural networks function, how to optimize, how to implement, and how to use them for applications. Due to the limited time, a simple hands-on overview without deep theoretical background is only possible, but all key concepts will be provided.

This tutorial is more like a lecture with the ability to follow all topics in an interactive format. Likewise to the ”Beginners guide to machine learning” a jupyter Notebook will be provided, to either directly during the session follow the individual steps or reproduce the whole session at home. Similar to a cookbook, the basic concepts of artificial neural networks can be learned based on examples.

The jupyter notebook: https://tinyurl.com/intro2ann