Kursthemen
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Lectures: On Mondays, 12-14
Exercises: On Tuesdays, 14-16
Location (both lectures and exercises): Seminarraum 2 in the new KI Gebäude, ground floor (Campus Nord, Emil-Fischer-Str. 50)
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Please find the PDF & the PPT here, as well as the video recording for the second part of the lecture on important CNN architectures.
The .pptx slides contain additional videos / animations that are not playing in the PDF.
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If you have questions or problems with the exercises, you can ask here for help.
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Exercise for Lecture 2
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15.4. L1: Introduction to Deep Learning & Course Organization (Dozent: Goran Glavaš)
22.4. L2: Building Blocks & Feed-forward Nets (Dozentin: Katharina Breininger)
29.4. L3: Optimization & Training (Dozent: Goran Glavaš)
13.5. L4: Convolutional networks (Dozentin: Katharina Breininger)
27.5. L5: Autoencoders & Generative Adversarial Networks (Dozent/in: Radu Timofte / Nancy Mehta )
3.6. L6: Recurrent Networks (Dozent: Andreas Hotho)
10.6. L7: Attention & Transformers (Dozent: Andreas Hotho)
17.6. L8: Intro to Reinforcement Learning (Dozent: Carlo D'Eramo)
24.6. L9: Deep Reinforcement Learning (Dozent: Carlo D'Eramo)
1.7. L10: Graph Representation Learning (Dozent: Ingo Scholtes)
8.7. L11: Neural Networks for/on Graphs (Dozent: Ingo Scholtes)
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