network: videos

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1. 1▶MIT 6.S191: Secrets of Massively Parallel TrainingMIT 6.S191: Introduction to Deep LearningNotes
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2. 2▶MIT 6.S191: AI for ScienceMIT 6.S191: Introduction to Deep LearningNotes
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3. 3▶MIT 6.S191: The Three Laws of AIMIT 6.S191: Introduction to Deep LearningNotes
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4. 4▶MIT 6.S191: Language Models and New FrontiersMIT 6.S191: Introduction to Deep LearningNotes
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5. 5▶Pragathi Jha, Modeling Cyber Adversaries: A Critical Survey of Methods and AssumptionsCERIAS Weekly Security Seminar - Purdue University49:53
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6. 6▶MIT 6.S191: Reinforcement LearningMIT 6.S191: Introduction to Deep LearningNotes
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7. 7▶Smriti Bhatt, Evolving Security Landscape in the Agentic AI-Enabled IoT EraCERIAS Weekly Security Seminar - Purdue University59:19
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8. 8▶MIT 6.S191: Deep Generative ModelingMIT 6.S191: Introduction to Deep LearningNotes
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9. 9▶Gary Hayslip, The AI Arms RaceCERIAS Weekly Security Seminar - Purdue University52:14
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10. 10▶MIT 6.S191: Convolutional Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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11. 11▶Brian Peretti, Symposium Closing Keynote: AI, Cybersecurity, and the Path ForwardCERIAS Weekly Security Seminar - Purdue University1:13:37
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12. 12▶MIT 6.S191: Recurrent Neural Networks, Transformers, and AttentionMIT 6.S191: Introduction to Deep LearningNotes
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13. 13▶Jen Sims, Analyzing Supply Chain Risk in Mobile Applications for Home Energy Storage SystemsCERIAS Weekly Security Seminar - Purdue University55:20
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14. 14▶MIT Introduction to Deep Learning | 6.S191MIT 6.S191: Introduction to Deep LearningNotes
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15. 15▶Kelechi Kalu, Software Signing in Practice: Lessons from Adoption and Usability Toward Broader Supply Chain TrustCERIAS Weekly Security Seminar - Purdue University1:03:42
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16. 16▶Ruqi Zhang, Discovering and Controlling AI Safety Risks in Foundation Models: A Probabilistic PerspectiveCERIAS Weekly Security Seminar - Purdue University59:26
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17. 17▶Danny Vukobratovich, ISO 27001 as the Engine, NIST CSF 2.0 as the Dashboard, A Practical Operating ModelCERIAS Weekly Security Seminar - Purdue University1:03:15
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18. 18▶Thai Le, Towards Robust and Trustworthy AI Speech Models: What You Read Isn't What You HearCERIAS Weekly Security Seminar - Purdue University38:41
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19. 19▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 16: Modeling pumps within pipe networksCE 356: Elements of Hydraulic EngineeringNotes
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20. 20▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 15: Pump combinations practiceCE 356: Elements of Hydraulic EngineeringNotes
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21. 21▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 14: Pump speed and pumps in combinationCE 356: Elements of Hydraulic EngineeringNotes
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22. 22▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 13: Pump power and efficiencyCE 356: Elements of Hydraulic EngineeringNotes
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23. 23▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 10: Looped pipe networksCE 356: Elements of Hydraulic EngineeringNotes
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24. 24▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 08: Energy equation practiceCE 356: Elements of Hydraulic EngineeringNotes
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25. 25▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 05: Computing frictional lossesCE 356: Elements of Hydraulic EngineeringNotes
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26. 26▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 04: Introduction to friction modelsCE 356: Elements of Hydraulic EngineeringNotes
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27. 27▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 03: Conservation of energyCE 356: Elements of Hydraulic EngineeringNotes
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28. 28▶CE356: Elements of hydraulic engineering (SP 2025), Lecture 02: Conservation of mass and momentumCE 356: Elements of Hydraulic EngineeringNotes
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29. 29▶Lesson 23: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 2Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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30. 30▶Lesson 22: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 1Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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31. 31▶Lesson 21: Network Algorithms and Approximations by Mohammad Hajiaghayi:Con Facility & Group SteinerNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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32. 32▶Lesson 20: Network Algorithms and Approximations by Mohammad Hajiaghayi: Metric Facility LocationNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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33. 33▶Lesson 19: Network Algorithms and Approximations by Mohammad Hajiaghayi: k-Center and k-MedianNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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34. 34▶Lesson 18: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network Streaming 2Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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35. 35▶MIT 6.S191 (2025): Language Models and New FrontiersMIT 6.S191: Introduction to Deep LearningNotes
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36. 36▶Lesson 17: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network StreamingNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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37. 37▶MIT 6.S191 (2025): Reinforcement LearningMIT 6.S191: Introduction to Deep LearningNotes
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38. 38▶Lesson 16: Network Algorithms and Approximations by Mohammad Hajiaghayi: Simplifying DecompositionsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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39. 39▶MIT 6.S191 (2025): Deep Generative ModelingMIT 6.S191: Introduction to Deep LearningNotes
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40. 40▶MIT 6.S191 (2025): Convolutional Neural NetworksMIT 6.S191: Introduction to Deep LearningNotes
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41. 41▶MIT 6.S191 (2025): Recurrent Neural Networks, Transformers, and AttentionMIT 6.S191: Introduction to Deep LearningNotes
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42. 42▶Lesson 13: Network Algorithms and Approximations by Mohammad Hajiaghayi: Tree Embedding Cut ProblemsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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43. 43▶MIT Introduction to Deep Learning (2025) | 6.S191MIT 6.S191: Introduction to Deep LearningNotes
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44. 44▶Lesson 12: Network Algorithms and Approximations by Mohammad Hajiaghayi: Oblivious RoutingNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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45. 45▶Lesson 11: Network Algorithms and Approximations by Mohammad Hajiaghayi: Oblivious and UniversalNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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46. 46▶Lesson 9: Network Algorithms and Approximations by Mohammad Hajiaghayi: Uniform Buy-at-Bulk NetworkNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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47. 47▶Lesson 8: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network Design ProblemsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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48. 48▶Lesson 7: Network Algorithms and Approximations by Mohammad Hajiaghayi: FRT Embeddings into TreesNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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49. 49▶Paris Perdikaris - PirateNets: Physics informed Deep Learning with Residual Adaptive NetworksPhysics-informed machine learning meets engineering seminar seriesNotes
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50. 50▶Tobias Heinrich Nagel - Kalman Bucy informed Neural Networks for System IdentificationPhysics-informed machine learning meets engineering seminar seriesNotes
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