Am Dienstag, den 13. November berichtet Herr Prof. Dr. Andreas Maier, der Friedrich-Alexander-Universität Erlangen-Nürnberg im Rahmen des SAN Folgetreffens "Change through Digitalisation" im Bürogebäude Zellescher Weg, Raum A152 um 16:45 Uhr über die Auswirkungen von "Deep Learning" und diskutiert im Anschluss die Gründe der Popularität.
"This presentation tries to give a gentle introduction to deep learning, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the percep-tron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Medical image processing and digital humanities are examples of areas which have been largely affected by this rapid progress. Mostly perceptual tasks have benefitted from this, as we show in our examples. However, there are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results.
Yet, some of these approaches neglect prior knowl-
edge and hence bear the risk of producing implausible
results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future."