Unterschiede

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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.
+	=> TODO
+	=> 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