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