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# 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