Deep Learning has transformed the ways in which we approach data science and machine learning in a variety of fields and disciplines substantially. Problems in computer vision, speech recognition and synthesis, protein folding prediction and many more fields can now more or less easily be tackled. In this talk, I will give a comprehensive introduction to the recent advances in the field and their relevance to concrete problems in medical and biological applications. As supervised learning in these domains usually falls short due to a lack of data, techniques for transfer learning and domain adaptation between datasets as well as the quantification of uncertainty in deep neural networks.
This talk was given in at the 4th HBP Summer School on Future Computing Brain Science and Artificial Intelligence in Obergurgle, Austria.