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First he wanted me to give an overview over the topics of the lecture by drawing a mindmap

Then he wanted me to explain the Perceptron

=> Schematic
=> XOR → MLP
=> Universal Function Approx
=> Multiple Layer: Deep Learning

Name a loss functions that we discussed?

=> L2 for regression, CE for classification, both optimal wrt. MLE
=> Derivation for L2 by assuming Gauss

How does the network learn now?

=> Backpropagation + gradient descend
=> Chain rule: Multiplication of gradients + weight update
	=> Exploding/vanishing gradients

What other method did we use to encode the Information? (Not quite sure about the wording here)

=> Activation Functions: Sigmoid/Tanh → ReLU 
	=> prevent vanishing gradients

What about Dying ReLU?

=> Leaky ReLU

What is an regularization alternative fighting the internal covariate shift?

=> Batchnormalization

Can you draw and explain the LSTM structure?

What are GANs?

=> Generator vs Discriminator trained by MiniMax

There was a problem called Mode Collapse, please explain it.

=> D focuses only on one feature → G also

Can you explain Cycle Consistent GANs?

=> Principle(trainiable inverse mapping) + combined loss function explained

What is an Autoencoder?

=> Encoder-Decoder
=> Undertetermined AE + Sparse AE

What is common for U-Net and Autoencoder? Whats the difference?

=> Same: Encoder-Decoder structure
=> Different: (Conv Layer), but mainly skip connections

How good would U-Net be in comparison to the AE?

=> thanks to the skip connections it could basically copy the input