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Lectures Syllabus

Date Lecture Teacher Materials Notes / Reading
22.02.2023 1. Introduction to Neural Networks BF slides
01.03.2023 2. The Power of Neural Networks BF slides
08.03.2023 3. Backpropagation AS slides
15.03.2023 4. Stochastic Gradient Descent (SGD) AS slides
22.03.2023 5. Convolutional Neural Networks AS slides
29.03.2023 6. Weight Initialization, Batch Normalization, Resnets, Transfer Learning BF slides
05.04.2023 7. Regularization Methods for NNs AS slides Goodfellow: 7.1-7.12
12.04.2023 8. Adaptive SGD Methods AS slides
19.04.2023 9. Adversarial Patterns. Robust Learning Approaches BF slides
26.04.2023 10. Learning Representations I: Metric Learning, Word Vectors AS slides
03.05.2023 11. Learning Representations II: t-SNE, Unsupervised RL AS slides
10.05.2023 — Rektor's day —
17.05.2023 12. Learning Representations III: Variational Autoencoders BF slides
24.05.2023 13. Recurrent Neural Networks, Transformer networks BF slides
courses/bev033dle/lectures.txt · Last modified: 2023/05/23 22:27 by flachbor