Speaker
Thomas Keck
(KIT)
Description
A summary of the history of deep-learning is given and the difference to traditional artificial neural networks is discussed.
Advanced methods like convoluted neural networks, recurrent neural networks and unsupervised training are introduced.
Interesting examples from this emerging field outside HEP are presented. Possible applications in HEP are discussed.
