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1. 1▶UofT GenAI Course -- Lecture 14: Maximum Likelihood LearningUofT -- Deep GenAI Course -- Summer25Notes
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2. 2▶UofT GenAI Course -- Lecture 13: Explicit Distribution Learning - SamplingUofT -- Deep GenAI Course -- Summer25Notes
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3. 3▶UofT GenAI Course -- Lecture 12: Naive Bayes -- Most Basic Generative ModelUofT -- Deep GenAI Course -- Summer25Notes
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4. 4▶UofT GenAI Course -- Lecture 11: Discriminative vs Generative LearningUofT -- Deep GenAI Course -- Summer25Notes
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5. 5▶UofT GenAI Course -- Lecture 10: Generic Problem of Data Generation -- Basic DefinitionsUofT -- Deep GenAI Course -- Summer25Notes
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6. 6▶UofT GenAI Course -- Lecture 9: Prompt Design for Task-specific Sampling from LLMsUofT -- Deep GenAI Course -- Summer25Notes
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7. 7▶UofT GenAI Course -- Lecture 8: Statistical View on Fine-Tuning and LoRAUofT -- Deep GenAI Course -- Summer25Notes
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8. 8▶UofT GenAI Course -- Lecture 7: Pre-training and Fine-tuningUofT -- Deep GenAI Course -- Summer25Notes
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9. 9▶UofT GenAI Course -- Lecture 6: Examples of LLMs -- Size, Data and Practical ConsiderationsUofT -- Deep GenAI Course -- Summer25Notes
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10. 10▶UofT GenAI Course -- Lecture 4: Extracting Language Context by Self-AttentionUofT -- Deep GenAI Course -- Summer25Notes
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11. 11▶UofT GenAI Course -- Lecture 5: LMs -- Transformer-based LMsUofT -- Deep GenAI Course -- Summer25Notes
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12. 12▶UofT GenAI Course -- Lecture 2: LMs -- Definition and Basic Bi-Gram ModelUofT -- Deep GenAI Course -- Summer25Notes
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13. 13▶UofT GenAI Course -- Lecture 3: Context-Aware LMs --Recurrent LMsUofT -- Deep GenAI Course -- Summer25Notes
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14. 14▶UofT GenAI Course -- Lecture 1: LMs -- Tokenization and EmbeddingUofT -- Deep GenAI Course -- Summer25Notes
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15. 15▶UofT GenAI Course -- Lecture 0: Course Overview and LogisticsUofT -- Deep GenAI Course -- Summer25Notes
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16. 16▶Lesson 13: Principles of Data Science by Mohammad Hajiaghayi: Data Science Ethics and Best PracticesPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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17. 17▶Lesson 12: Principles of Data Science by Mohammad Hajiaghayi: Information VisualizationPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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18. 18▶Lesson 10: Principles of Data Science by Mohammad Hajiaghayi: Entity Resolution, Missing DataPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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19. 19▶Lesson 9: Principles of Data Science by Mohammad Hajiaghayi: Data Wrangling and CleaningPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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20. 20▶Lesson 8: Principles of Data Science by Mohammad Hajiaghayi: Data Modeling, SQL & NoSQLPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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21. 21▶Lesson 7: Principles of Data Science by Mohammad Hajiaghayi: Data Modeling & Computation ToolsPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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22. 22▶Lesson 6: Principles of Data Science by Mohammad Hajiaghayi: Data Collection and Loading ToolsPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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23. 23▶Lesson 5: Principles of Data Science by Mohammad Hajiaghayi: Basic Statistics and ML AlgorithmsPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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24. 24▶Lesson 4: Principles of Data Science by Mohammad Hajiaghayi: Basic Concepts and Tools, Part 2Principles of Data Science Course by Mohammad HajiaghayiNotes
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25. 25▶Lesson 3: Principles of Data Science by Mohammad Hajiaghayi: Basic Concepts and Tools, Part 1Principles of Data Science Course by Mohammad HajiaghayiNotes
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26. 26▶Lesson 2: Principles of Data Science by Mohammad Hajiaghayi: Introduction and Further MotivationsPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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27. 27▶Lesson 1: Principles of Data Science by Mohammad Hajiaghayi: Introduction to Data SciencePrinciples of Data Science Course by Mohammad HajiaghayiNotes
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28. 28▶Lecture 3 - Image FilteringCAP5415 Computer Vision - Fall 2023Notes
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29. 29▶Lecture 2 - Linear Algebra BasicsCAP5415 Computer Vision - Fall 2023Notes
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30. 30▶Lecture 1 - Introduction to Computer VisionCAP5415 Computer Vision - Fall 2023Notes
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31. 31▶Lecture 3.2 - Image Filtering [Digitization of 1D, 2D, 3D, and Arc]CAP5415 Computer Vision - Fall 2023Notes
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32. 32▶Lecture 3.1 - Image Filtering [Digitization]CAP5415 Computer Vision - Fall 2023Notes
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33. 33▶Lecture 2.2 - Linear Algebra Basics [Matrix]CAP5415 Computer Vision - Fall 2023Notes
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34. 34▶Lecture 2.1 Linear Algebra Basics [Vectors]CAP5415 Computer Vision - Fall 2023Notes
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35. 35▶Lecture 2.3 - Linear Algebra Basics [Transformations]CAP5415 Computer Vision - Fall 2023Notes
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36. 36▶Lecture 2.4 - Linear Algebra Basics [Single Value Decomposition]CAP5415 Computer Vision - Fall 2023Notes
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37. 37▶Lecture 2.5 - Linear Algebra Basics [Q&A]CAP5415 Computer Vision - Fall 2023Notes
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38. 38▶Lecture 1.5 - Introduction to Computer Vision [Complex Computer Vision Tasks]CAP5415 Computer Vision - Fall 2023Notes
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39. 39▶Lecture 1.2 - Introduction to Computer Vision [Digital Image]CAP5415 Computer Vision - Fall 2023Notes
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40. 40▶Lecture 1.4 - Introduction to Computer Vision [Object Detection]CAP5415 Computer Vision - Fall 2023Notes
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41. 41▶Lecture 1.1 - Introduction to Computer Vision [Basics]CAP5415 Computer Vision - Fall 2023Notes
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42. 42▶Lecture 1.3 - Introduction to Computer Vision [Motivation]CAP5415 Computer Vision - Fall 2023Notes
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43. 43▶Lesson 11: Principles of Data Science by Mohammad Hajiaghayi: NLP & Word2VecPrinciples of Data Science Course by Mohammad HajiaghayiNotes
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44. 44▶CS 182: Lecture 2, Part 3: Machine Learning BasicsDeep Learning: CS 182 Spring 2021Notes
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45. 45▶CS 182: Lecture 1, Part 3: IntroductionDeep Learning: CS 182 Spring 2021Notes
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46. 46▶CS 182: Lecture 2, Part 1: Machine Learning BasicsDeep Learning: CS 182 Spring 2021Notes
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47. 47▶CS 182: Lecture 2, Part 2: Machine Learning BasicsDeep Learning: CS 182 Spring 2021Notes
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48. 48▶CS 182: Lecture 4: Part 1: OptimizationDeep Learning: CS 182 Spring 2021Notes
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49. 49▶CS 182: Lecture 5: Part 2: BackpropagationDeep Learning: CS 182 Spring 2021Notes
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50. 50▶CS 182: Lecture 1, Part 2: IntroductionDeep Learning: CS 182 Spring 2021Notes
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