lec: videos

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1. 1▶LLMs | Tokenization Strategies | Lec 9Large Language ModelsNotes
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2. 2▶LLMs | Advanced Attention Mechanisms-II | Lec 8.2Large Language ModelsNotes
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3. 3▶LLMs | Advanced Attention Mechanisms-I | Lec 8.1Large Language ModelsNotes
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4. 4▶LLMs | Pre-training Strategies | ELMo & BERT | Lec 7Large Language ModelsNotes
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5. 5▶LLMs | Intro to Transformer: Positional Encoding and Layer Normalization | Lec 6.2Large Language ModelsNotes
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6. 6▶LLMs | Introduction to Transformer: Self & Multi-Head Attention | Lec 6.1Large Language ModelsNotes
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7. 7▶LLMs | Neural Language Models: Seq2Seq and Attention | Lec 5.3Large Language ModelsNotes
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8. 8▶LLMs | Neural Language Models: LSTM and GRU | Lec 5.2Large Language ModelsNotes
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9. 9▶LLMs | Neural Language Models: RNNs | Lec 5.1Large Language ModelsNotes
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10. 10▶LLMs | Word Representation: GloVe | Lec 4.2Large Language ModelsNotes
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11. 11▶LLMs | Word Representation: Word2Vec | Lec 4.1Large Language ModelsNotes
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12. 12▶LLMs | Language Models: Advanced Smoothing & Evaluation | Lec 3.2Large Language ModelsNotes
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13. 13▶LLMs | Introduction to Language Models| Lec 3.1Large Language ModelsNotes
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14. 14▶LLMs | Introduction to Natural Language Processing | Lec 02Large Language ModelsNotes
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15. 15▶LLMs | Introduction and Recent Advances | Lec 01Large Language ModelsNotes
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16. 16▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 16: reviewUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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17. 17▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 15: linear mixed modelUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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18. 18▶STATS 205 - Hierarchical Linear Models - Lecture 14: zero-inflated count regression; random effectsUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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19. 19▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 13: quasi-Poisson/neg binomial regressionUCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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20. 20▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 12 (binary response, Poisson regression)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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21. 21▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 11 (score, LRT, Wald test; GLM inference)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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22. 22▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 10 (IRLS; goodness of fit of GLM)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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23. 23▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lec 9 (Iteratively Reweighted Least Squares)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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24. 24▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 8 (Newton-Raphson; Fisher scoring)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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25. 25▶STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 7 (GLM exponential family)UCLA STATS 205 - Hierarchical Linear Models (Spring 2024)Notes
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26. 26▶[Generative AI & Engineering Application] Lec 16. Gen AI for Image, GAN Architecture & Loss FunctionChat GPT-X and Generative Models for EM Systems DesignNotes
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27. 27▶[Generative AI & Engineering Application] Lec 15. Cross-attention in transformer encoder-decoderChat GPT-X and Generative Models for EM Systems DesignNotes
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28. 28▶[Generative AI & Engineering Application] Lec 14. Multi-head attentions in transformer encoderChat GPT-X and Generative Models for EM Systems DesignNotes
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29. 29▶[Generative AI & Engineering Application] Lec 13. Attention is all you needChat GPT-X and Generative Models for EM Systems DesignNotes
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30. 30▶[Generative AI & Engineering Application] Lec 12. Back Propagations in RNNChat GPT-X and Generative Models for EM Systems DesignNotes
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31. 31▶[Generative AI & Engineering Application] Lec 11. Sequential generative AI with RNNChat GPT-X and Generative Models for EM Systems DesignNotes
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32. 32▶[Generative AI & Engineering Application] Lec 10. Overall Perspectives of Gen-AI ArchitectureChat GPT-X and Generative Models for EM Systems DesignNotes
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33. 33▶[Generative AI & Engineering Application] Lec 9. Generative AI Architecture & Entropy LossChat GPT-X and Generative Models for EM Systems DesignNotes
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34. 34▶[Generative AI & Engineering Application] Lec 7. GPT-collaborated IL for Hybrid Bonding TSVChat GPT-X and Generative Models for EM Systems DesignNotes
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35. 35▶[Generative AI & Engineering Application] Lec 6. Loss Function of Generative AI (Entropy & KLD)Chat GPT-X and Generative Models for EM Systems DesignNotes
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36. 36▶CSE101, Fall 22, Lec 12, part 1: Range search in balanced BSTsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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37. 37▶CSE101, Fall 22, Lec 11: AVL treesCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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38. 38▶CSE101, Fall 22, Lec 10, Part 1: Tree traversalsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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39. 39▶CSE101, Fall 22, Lec 10, Part 2: Self balancing trees and rotationsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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40. 40▶CSE101, Fall 22: Lec 6, asymptotic running time analysis of quadratic time sorting algorithmsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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41. 41▶CSE101, Fall 22, Lec 9: Binary Search TreesCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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42. 42▶CSE101, Fall 22, Lec 8: Binary heapsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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43. 43▶CSE101, Fall 22, Lec 7, Part 1: Analysis of binary searchCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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44. 44▶CSE101, Fall 22, Lec 7, Part 2: An introduction to priority queues and binary heapsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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45. 45▶CSE101, Fall 22, Lec 5: Starting asymptotic analysis of algorithms, big-Oh notationCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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46. 46▶CSE101, Fall 22, Lec 4: Stacks and recursionCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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47. 47▶CSE101 Fall 22, Lec 3, Part 3: An introduction to simulating recursion through stacksCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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48. 48▶CSE101 Fall 22, Lec 3, Part 2: An introduction to stacks and parentheses checkingCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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49. 49▶CSE101 Fall 22, Lec 3, Part 1: Reversing a linked listCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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50. 50▶CSE101, Fall 22: Linked listsCSE101, Fall 2022, Introduction to Data Structures and AlgorithmsNotes
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