In this lecture we will take a closer look at selected topics in deep learning with a focus on computer vision, covering theoretical and practical aspects. We will discuss design principles and properties of selected neural networks starting with classifiers and then extending to selected applications like object recognition, image registration and also discuss the application of computer vision based technologies to non-standard visual domains like scanned documents and spectrograms. In the second part of the course we turn to the more theoretical works of the field, addressing currently open questions. We will exemplarily discuss topics such as deep generative models, adversarial examples, introspection, and explainability. Finally we will provide practical guidelines for developing deep learning solutions based on the current state of research as well industry experience.
There are no hard requirements for this course, but a basic knowledge of neural networks (like backpropagation, CNNs, RNNs) and experience with one of the common DL frameworks (like TensorFlow or Torch) is assumed. The ideal participant would have finished “Implementing ANNs in Tensorflow” or some comparable course before visiting this lecture.