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# Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen gezeigt.

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pruefungen:hauptstudium:ls5:dl-august-19 [29.08.2019 13:35] (aktuell) Muetzi angelegt |
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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 |