andrew ng

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1. 1▶Take back control of your AI coding workflowDeepLearning.AI CoursesNotes
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2. 2▶AI writes your code. Who reviews it?DeepLearning.AI CoursesNotes
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3. 3▶Fast inference changes what you can buildDeepLearning.AI CoursesNotes
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4. 4▶Voice for AI Agents and ApplicationsDeepLearning.AI CoursesNotes
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5. 5▶Optimize, deploy, and benchmark an open-source LLM with vLLMDeepLearning.AI CoursesNotes
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6. 6▶Build Your Own App In Just 30 Minutes! Full Course with Andrew NgDeepLearning.AI CoursesNotes
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7. 7▶Build Visual AI AgentsDeepLearning.AI CoursesNotes
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8. 8▶Full AI Prompting Course with Andrew NgDeepLearning.AI CoursesNotes
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9. 9▶The Ultimate Transformer Course for Working EngineersDeepLearning.AI CoursesNotes
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10. 10▶Build Interactive Agents with Generative UIDeepLearning.AI CoursesNotes
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11. 11▶Just deployed to production… and leaked all the credit cards 😬DeepLearning.AI CoursesNotes
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12. 12▶📉 Turn your multimodal data into something you can actually queryDeepLearning.AI CoursesNotes
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13. 13▶Coming Soon: Build Interactive Agents with Generative UIDeepLearning.AI CoursesNotes
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14. 14▶New course! Spec-Driven DevelopmentDeepLearning.AI CoursesNotes
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15. 15▶Boost LLM performance: New SGLang course is live 🚀DeepLearning.AI CoursesNotes
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16. 16▶Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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17. 17▶Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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18. 18▶Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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19. 19▶Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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20. 20▶Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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21. 21▶Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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22. 22▶Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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23. 23▶Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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24. 24▶Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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25. 25▶Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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26. 26▶Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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27. 27▶Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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28. 28▶Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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29. 29▶Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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30. 30▶Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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31. 31▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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32. 32▶Geoffrey Hinton: "Does the Brain do Inverse Graphics?"GSS2012: Deep Learning, Feature LearningNotes
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33. 33▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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34. 34▶Alan Yuille: "Compositional Models"GSS2012: Deep Learning, Feature LearningNotes
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35. 35▶Geoffrey Hinton: "A Computational Principle that Explains Sex, the Brain, and Sparse Coding"GSS2012: Deep Learning, Feature LearningNotes
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36. 36▶Yann LeCun: "Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction, Pt. 3"GSS2012: Deep Learning, Feature LearningNotes
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37. 37▶Geoffrey Hinton: "Some Applications of Deep Learning"GSS2012: Deep Learning, Feature LearningNotes
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38. 38▶Andrew Ng: "Non-linear Hypotheses, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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39. 39▶Andrew Ng: "Non-linear Hypotheses, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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40. 40▶Andrew Ng: "Advanced Topics + Research Philosophy / Neural Networks: Representation"GSS2012: Deep Learning, Feature LearningNotes
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