language model: videos

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1. 1▶MIT 6.S191: Secrets of Massively Parallel TrainingAlexander AminiNotes
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2. 2▶MIT 6.S191: AI for ScienceAlexander AminiNotes
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3. 3▶MIT 6.S191: The Three Laws of AIAlexander AminiNotes
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4. 4▶MIT 6.S191: Language Models and New FrontiersAlexander AminiNotes
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5. 5▶MIT 6.S191: Reinforcement LearningAlexander AminiNotes
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6. 6▶MIT 6.S191: Deep Generative ModelingAlexander AminiNotes
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7. 7▶MIT 6.S191: Convolutional Neural NetworksAlexander AminiNotes
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8. 8▶MIT 6.S191: Recurrent Neural Networks, Transformers, and AttentionAlexander AminiNotes
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9. 9▶MIT Introduction to Deep Learning | 6.S191Alexander AminiNotes
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10. 10▶I BUILT A FULLY AUTOMATIC MANSPLAINERYannic KilcherNotes
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11. 11▶Traditional X-Mas StreamYannic KilcherNotes
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12. 12▶Traditional Holiday Live StreamYannic KilcherNotes
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13. 13▶TiDAR: Think in Diffusion, Talk in Autoregression (Paper Analysis)Yannic KilcherNotes
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14. 14▶MMXXV: Exit Berlin{ lucidbeaming: "dev" }Notes
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15. 15▶Titans: Learning to Memorize at Test Time (Paper Analysis)Yannic KilcherNotes
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16. 16▶[Paper Analysis] The Free Transformer (and some Variational Autoencoder stuff)Yannic KilcherNotes
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17. 17▶[Video Response] What Cloudflare's code mode misses about MCP and tool callingYannic KilcherNotes
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18. 18▶[Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant)Yannic KilcherNotes
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19. 19▶AGI is not coming!Yannic KilcherNotes
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20. 20▶Context Rot: How Increasing Input Tokens Impacts LLM Performance (Paper Analysis)Yannic KilcherNotes
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21. 21▶Energy-Based Transformers are Scalable Learners and Thinkers (Paper Review)Yannic KilcherNotes
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22. 22▶MIT 6.S191 (2025): AI for Biology (Microsoft)Alexander AminiNotes
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23. 23▶On the Biology of a Large Language Model (Part 2)Yannic KilcherNotes
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24. 24▶MIT 6.S191 (2025): A Hipocratic Oath, for *your* AI (Comet ML)Alexander AminiNotes
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25. 25▶MIT 6.S191 (2025): Large Language Models (Liquid AI)Alexander AminiNotes
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26. 26▶MIT 6.S191 (2025): Large Language Models (Google)Alexander AminiNotes
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27. 27▶MIT 6.S191 (2025): Language Models and New FrontiersAlexander AminiNotes
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28. 28▶On the Biology of a Large Language Model (Part 1)Yannic KilcherNotes
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29. 29▶MIT 6.S191 (2025): Reinforcement LearningAlexander AminiNotes
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30. 30▶[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsYannic KilcherNotes
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31. 31▶Traditional Holiday Live StreamYannic KilcherNotes
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32. 32▶L15: Pre-training & fine tuning | gpt architecture adaptation & text generationIntroduction to large language modelsNotes
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33. 33▶L14: Causal language modelling | decoder only transformers & autoregressive text generationIntroduction to large language modelsNotes
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34. 34▶L13: Transformers for language modelling | encoder only decoder only & encoder decoder architecturesIntroduction to large language modelsNotes
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35. 35▶L11: Language modelling | pre training foundation for large language modelsIntroduction to large language modelsNotes
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36. 36▶L12: Introduction to language modelling - motivation | fine tuning in GPT & transformersIntroduction to large language modelsNotes
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37. 37▶L10: Layer normalization | normalization in transformers encoder decoder architecture explainedIntroduction to large language modelsNotes
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38. 38▶L8: Batch normalization | residual connections and layer normalization in transformersIntroduction to large language modelsNotes
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39. 39▶L5: Sinusoidal encoding & sequence orderIntroduction to large language modelsNotes
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40. 40▶L6: Zooming into decoder layer | decoding transformers masked self attention &cross attentionIntroduction to large language modelsNotes
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41. 41▶L7: Positional encoding motivation methods & limitationsIntroduction to large language modelsNotes
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42. 42▶L9: Teacher forcing & masked attention | autoregressive decoding with masking in transformersIntroduction to large language modelsNotes
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43. 43▶L4: Multi-headed attention in transformers explainedIntroduction to large language modelsNotes
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44. 44▶L3: Self-attention in transformers encoder & contextual word embeddingsIntroduction to large language modelsNotes
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45. 45▶L2: Attention is all you need transformer architecture explainedIntroduction to large language modelsNotes
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46. 46▶L1: Introduction to transformer architectureIntroduction to large language modelsNotes
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47. 47▶MMXXIV: Turpentine and tea{ lucidbeaming: "dev" }Notes
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48. 48▶Lecture15 - Generating New Molecules - MLCB24MLCB24 - Machine Learning in Computational Biology Fall 2024Notes
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49. 49▶Lecture14 - Chemistry GNNs - MLCB24MLCB24 - Machine Learning in Computational Biology Fall 2024Notes
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50. 50▶Lecture13 - Drug Development Intro - MLCB24MLCB24 - Machine Learning in Computational Biology Fall 2024Notes
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