deep learning: videos

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1. 1▶0.27 Flow of the ProjectCarnegie Mellon University Deep LearningNotes
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2. 2▶0.26 Workflow of HWpart-2sCarnegie Mellon University Deep LearningNotes
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3. 3▶0.26 Workflow of HWpart-1sCarnegie Mellon University Deep LearningNotes
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4. 4▶0.25 Saving & Loading ModelCarnegie Mellon University Deep LearningNotes
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5. 5▶0.24 Distributed TrainingCarnegie Mellon University Deep LearningNotes
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6. 6▶CMU Introduction To Deep Learning 11-785, Fall 2026: Lecture 0Carnegie Mellon University Deep LearningNotes
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7. 7▶0.23 PipelinesCarnegie Mellon University Deep LearningNotes
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8. 8▶0.22 Block ProcessingCarnegie Mellon University Deep LearningNotes
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9. 9▶0.21 Losses Part 1Carnegie Mellon University Deep LearningNotes
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10. 10▶0.20 What to Do When StrugglingCarnegie Mellon University Deep LearningNotes
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11. 11▶0.19 Debugging your modelCarnegie Mellon University Deep LearningNotes
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12. 12▶0.18 WandbCarnegie Mellon University Deep LearningNotes
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13. 13▶But what is cross-entropy? | Compression is Intelligence Part 2Neural networksNotes
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14. 14▶MIT 6.S191: Secrets of Massively Parallel TrainingMIT 6.S191: Introduction to Deep LearningNotes
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15. 15▶MIT 6.S191: AI for ScienceMIT 6.S191: Introduction to Deep LearningNotes
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16. 16▶MIT 6.S191: The Three Laws of AIMIT 6.S191: Introduction to Deep LearningNotes
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17. 17▶MIT 6.S191: Language Models and New FrontiersMIT 6.S191: Introduction to Deep LearningNotes
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18. 18▶MIT 6.S191: Reinforcement LearningMIT 6.S191: Introduction to Deep LearningNotes
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19. 19▶MIT 6.S191: Deep Generative ModelingMIT 6.S191: Introduction to Deep LearningNotes
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20. 20▶MIT 6.S191: Convolutional Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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21. 21▶MIT 6.S191: Recurrent Neural Networks, Transformers, and AttentionMIT 6.S191: Introduction to Deep LearningNotes
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22. 22▶MIT Introduction to Deep Learning | 6.S191MIT 6.S191: Introduction to Deep LearningNotes
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23. 23▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 1: IntroductionStanford CS231N Deep Learning for Computer Vision I 2025Notes
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24. 24▶Stanford CS231N | Spring 2025 | Lecture 2: Image Classification with Linear ClassifiersStanford CS231N Deep Learning for Computer Vision I 2025Notes
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25. 25▶Stanford CS231N | Spring 2025 | Lecture 3: Regularization and OptimizationStanford CS231N Deep Learning for Computer Vision I 2025Notes
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26. 26▶Stanford CS231N | Spring 2025 | Lecture 4: Neural Networks and BackpropagationStanford CS231N Deep Learning for Computer Vision I 2025Notes
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27. 27▶Stanford CS231N | Spring 2025 | Lecture 5: Image Classification with CNNsStanford CS231N Deep Learning for Computer Vision I 2025Notes
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28. 28▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 6: CNN ArchitecturesStanford CS231N Deep Learning for Computer Vision I 2025Notes
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29. 29▶Stanford CS231N | Spring 2025 | Lecture 7: Recurrent Neural NetworksStanford CS231N Deep Learning for Computer Vision I 2025Notes
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30. 30▶Stanford CS231N | Spring 2025 | Lecture 8: Attention and TransformersStanford CS231N Deep Learning for Computer Vision I 2025Notes
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31. 31▶Stanford CS231N | Spring 2025 | Lecture 9: Object Detection, Image Segmentation, VisualizingStanford CS231N Deep Learning for Computer Vision I 2025Notes
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32. 32▶Stanford CS231N | Spring 2025 | Lecture 11: Large Scale Distributed TrainingStanford CS231N Deep Learning for Computer Vision I 2025Notes
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33. 33▶Stanford CS231N | Spring 2025 | Lecture 12: Self-Supervised LearningStanford CS231N Deep Learning for Computer Vision I 2025Notes
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34. 34▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 13: Generative Models 1Stanford CS231N Deep Learning for Computer Vision I 2025Notes
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35. 35▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 14: Generative Models 2Stanford CS231N Deep Learning for Computer Vision I 2025Notes
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36. 36▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 15: 3D VisionStanford CS231N Deep Learning for Computer Vision I 2025Notes
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37. 37▶Stanford CS231N Deep Learning for Computer Vision | Spring 2025 | Lecture 10: Video UnderstandingStanford CS231N Deep Learning for Computer Vision I 2025Notes
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38. 38▶But how do AI images and videos actually work? | Guest video by Welch LabsNeural networksNotes
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39. 39▶MIT 6.S191 (2025): Language Models and New FrontiersMIT 6.S191: Introduction to Deep LearningNotes
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40. 40▶MIT 6.S191 (2025): Reinforcement LearningMIT 6.S191: Introduction to Deep LearningNotes
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41. 41▶MIT 6.S191 (2025): Deep Generative ModelingMIT 6.S191: Introduction to Deep LearningNotes
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42. 42▶MIT 6.S191 (2025): Convolutional Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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43. 43▶MIT 6.S191 (2025): Recurrent Neural Networks, Transformers, and AttentionMIT 6.S191: Introduction to Deep LearningNotes
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44. 44▶Stanford CS224N: NLP w/ DL | Spring 2024 | Lecture 14 - Reasoning and Agents by Shikhar MurtyStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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45. 45▶Stanford CS224N: NLP w/ DL| Spring 2024 | Lecture 13 - Brain-Computer Interfaces, Chaofei FanStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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46. 46▶Stanford CS224N: NLP w/ DL | Spring 2024 | Lecture 12 - Efficient Training, Shikhar MurtyStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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47. 47▶Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 11 - Benchmarking by Yann DuboisStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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48. 48▶Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 10 - Post-training by Archit SharmaStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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49. 49▶Stanford CS224N: NLP w/ DL | Spring 2024 | Lecture 7 - Attention, Final Projects and LLM IntroStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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50. 50▶Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 6 - Sequence to Sequence ModelsStanford CS224N Natural Language Processing with Deep Learning I Spring 2024 I Professor Christopher ManningNotes
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