====== Deep Learning - Summer Term 2018 ======

Examiner: Prof. Maier

  * Give an overview of the topics of the lecture (I drew a mindmap of all lecture topics)
  * What problems can't be solved by a perceptron? -> XOR.
  * What can we do to solve it?
  * Universal approximation theorem. How good can we approximate? How can we better approximate?
  * Why do we need deeper models when we can already model any function with one layer?
  * Explain backpropagation
  * What loss function did we use? Cross entropy loss (classification, multinoulli distribution), L2 loss (regression, gaussian distribution)
  * Short derivation wanted for the loss functions.
  * We talked about hinge loss in the lecture. Can you say something about this? -> SVM
  * Can you explain some different optimizers? (I talked about Momentum, NAG and Adam)
  * Name some milestone architectures and say what new ingredient they introduced (I talked about /mentioned LeNet, AlexNet, Network in Network, VGG, ResNet)
  * Then he wanted me to explain ResNet in detail and asked some more questions about it
  * Explain LSTM. He wanted me to draw the block diagram
  * Explain YOLO.
  * How is segmentation working with deep learning?
  * Explain autoencoders

Prof. Maier was very friendly. For preparation I wrote my own summary, this was quite helpful