deep learning part

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1. 1▶CS 198-126: Lecture 15 - Vision TransformersCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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2. 2▶CS 198-126: Lecture 14 - Transformers and AttentionCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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3. 3▶CS 198-126: Lecture 13 - Intro to Sequence ModelingCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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4. 4▶CS 198-126: Lecture 12 - Diffusion ModelsCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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5. 5▶CS 198-126: Lecture 11 - Advanced GANsCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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6. 6▶CS 198-126: Lecture 10 - GANsCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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7. 7▶CS 198-126: Lecture 9 - Autoencoders, VAEs, Generative ModelingCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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8. 8▶CS 198-126: Lecture 8 - Semantic SegmentationCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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9. 9▶CS 198-126: Lecture 7 - Object DetectionCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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10. 10▶CS 198-126: Lecture 6 - Advanced Computer Vision ArchitecturesCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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11. 11▶CS 198-126: Lecture 5 - Intro to Computer VisionCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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12. 12▶CS 198-126: Lecture 4 - Intro to Pretraining and AugmentationsCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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13. 13▶CS 198-126: Lecture 3 - Intro to Deep Learning, Part 2CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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14. 14▶CS 198-126: Lecture 2 - Intro to Deep Learning, Part 1CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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15. 15▶CS 198-126: Lecture 1 - Intro to Machine LearningCS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)Notes
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16. 16▶NS 8: Statistical and Causal ModelsSargur SrihariNotes
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17. 17▶NS 7: Counterfactual Reasoning in Artificial IntelligenceSargur SrihariNotes
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18. 18▶NS 6: Causality in Artificial IntelligenceSargur SrihariNotes
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19. 19▶NS 5: Learning Visual Concepts using Natural SupervisionSargur SrihariNotes
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20. 20▶NS 4: Hybrid Architectures for Robust Artificial IntelligenceSargur SrihariNotes
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21. 21▶NS 3: Robust Artificial IntelligenceSargur SrihariNotes
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22. 22▶NS 2 Research Topics in Thinking Fast, Thinking SlowSargur SrihariNotes
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23. 23▶NS 1 Neurosymbolic Computing: Thinking Fast, Thinking SlowSargur SrihariNotes
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24. 24▶ML 16 Ethics of Artificial IntelligenceSargur SrihariNotes
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25. 25▶DL 12.4.5:7 NLP: Neural Translation, Attention Models and TransformersSargur SrihariNotes
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