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Lectures Syllabus

Date Lecture Teacher Materials Reading
21.02.2024 1. Introduction to Neural Networks BF slides McCulloch-Pitts Neuron
28.02.2024 2. The Power of Neural Networks BF slides
06.03.2024 3. Backpropagation AS slides Implicit Layers, Chapter 1
13.03.2024 4. Stochastic Gradient Descent (SGD) AS slides
20.03.2024 5. Convolutional Neural Networks AS slides Geometric Deep Learning
27.03.2024 6. Weight Initialization, Batch Normalization, Resnets, Transfer Learning BF slides
03.04.2024 7. Regularization Methods for NNs AS slides Goodfellow Chapter 7
10.04.2024 8. Adaptive SGD Methods AS slides
17.04.2024 9. Adversarial Patterns. Robust Learning Approaches BF slides
24.04.2024 10. Learning Representations I: Metric Learning, Word Vectors AS slides
01.05.2024 — Holiday —
08.05.2024 — Holiday, replacement day Thu 09.05.2024 —
09.05.2024 11. Learning Representations II: t-SNE, Unsupervised RL AS slides
15.05.2024 12. Learning Representations III: Variational Autoencoders BF slides
22.05.2024 13. Recurrent Neural Networks, Transformer networks BF slides
courses/bev033dle/lectures.txt · Last modified: 2024/05/21 19:55 by flachbor