 |
Weizmann Institute of ScienceDeep Learning for Computer Vision:Fundamentals and Applications |
 |
| Lecture | Topic | Instructor | Course Materials | Assignments |
| 1 | Introduction. Basics of ML | Assaf |
slides (pdf) video |
1st assignment |
| 2 | Classification methods | Lior |
slides (pdf) video |
|
| 3 | Neural networks | Assaf |
slides (pdf)
video |
|
| 4 | PyTorch | Rafail |
slides (pdf)
video |
5 | Convolutional neural networks | Assaf |
slides (pdf)
video |
2nd assignment |
| 6 | Practical training | Shai |
slides (pdf)
video weight init notebook |
|
| 7 | Architectures | Rafail |
slides (pdf)
video |
|
| 8 | Visualizing and understanding neural networks | Tali |
slides (pdf)
video |
|
| 9 | Advanced PyTorch | Rafail |
slides (pdf)
video |
3rd assignment |
| 10 | Adversarial examples | Niv Haim |
slides (pdf)
video |
|
| 11 | Sequences and Attention | Shai |
slides (pdf)
video (older version) |
|
| 12 | Vision Transformers | Shai |
slides (pdf)
video |
|
| 13 | Generative Models | Assaf |
slides (pdf)
video |
|
| 14 | Advanced Generative Models | Tali |
slides (pdf)
video |
4th assignment |
| 15 | Self supervision (I) | Tali |
slides (pdf)
video |
|
| 16 | Self supervision (II) | Shai |
slides (pdf)
video |
|
| 17 | Computer Graphics and Rendering | Meirav |
slides (pdf)
video |
|
| 18 | Implicit 3D representations (I) | Meirav |
slides (pdf)
video |
|
| 19 | Implicit representations (II) | Lior |
slides (pdf)
video |
|
| 20 | Learning from videos | Tali |
slides (pdf)
video |
|
| 21 | Detection and Segmentation (I) | Shai |
slides (pdf)
video |
|
| 22 | Image and Text | Rafail |
slides (pdf)
video |
|
| 23 | Detection and Segmentation (II) | Lior |
slides (pdf)
video |
24 | Parallel GPU | Shai |
slides (pdf)
video |