Video links

The lecture recordings of the courses in the course links, arranged by chapter. The reading plan gives the matching slides and says which parts of a lecture to watch.

Video or playlist	Link	Notes
Justin Johnson, Deep Learning for Computer Vision (UMich EECS 498-007, Fall 2019)	Playlist	22 recorded lectures; the numbers below are lecture numbers.
François Fleuret, Deep Learning (UNIGE 14x050)	Course page	Each numbered section has a short screencast linked next to its slides on the course page.
Jure Leskovec, Machine Learning with Graphs (Stanford CS224W, 2021)	Playlist	For the optional chapter 13.

By chapter

Chapter	UMich EECS 498-007 lectures	Other recordings
1. Deep Feedforward Networks	5: Neural networks	UNIGE 3.4, 6.1
2. Initialization and Signal Propagation	10: Training neural networks I	UNIGE 5.5, 6.2 · Karpathy, Building makemore part 3
3. Optimization for Deep Networks	4: Optimization · 11: Training neural networks II	UNIGE 5.2, 5.3
4. Normalization and Residual Connections	8: CNN architectures	UNIGE 6.4, 6.5
5. Regularization and Generalization in Deep Networks	10: Training neural networks I · 11: Training neural networks II	UNIGE 5.4, 6.3
6. Convolutional Networks	7: Convolutional networks	UNIGE 4.4, 4.5
7. Convolutional Architectures and Transfer Learning	8: CNN architectures · 11: Training neural networks II	UNIGE 8.1, 8.2, 8.5
8. Recurrent Networks	12: Recurrent networks	UNIGE 12.1, 12.2
9. Attention and Transformers	13: Attention	UNIGE 13.1–13.3
10. Self-Supervised Representation Learning	—	UNIGE 7.2, 7.3
11. Training at Scale and Efficient Inference	9: Hardware and software	UNIGE 6.6
12. Practical Methodology	11: Training neural networks II	UNIGE 5.6
13. Graph Neural Networks	—	CS224W lectures 6–9 in the playlist
14. Detection and Segmentation	15: Object detection · 16: Detection and segmentation	UNIGE 7.1, 8.3, 8.4
15. Infinite Width and the Neural Tangent Kernel	—	—