andrew: videos

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1. 1▶My YouTube Channel is Moving to Vimeo to Prevent You From Seeing AdvertisementsAndrew KelleyNotes
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2. 2▶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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3. 3▶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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4. 4▶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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5. 5▶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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6. 6▶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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7. 7▶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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8. 8▶Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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9. 9▶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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10. 10▶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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11. 11▶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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12. 12▶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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13. 13▶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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14. 14▶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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15. 15▶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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16. 16▶Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018Notes
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17. 17▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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18. 18▶Geoffrey Hinton: "Does the Brain do Inverse Graphics?"GSS2012: Deep Learning, Feature LearningNotes
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19. 19▶Rob Fergus: "Deep Learning Methods for Vision, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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20. 20▶Alan Yuille: "Compositional Models"GSS2012: Deep Learning, Feature LearningNotes
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21. 21▶Geoffrey Hinton: "A Computational Principle that Explains Sex, the Brain, and Sparse Coding"GSS2012: Deep Learning, Feature LearningNotes
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22. 22▶Yann LeCun: "Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction, Pt. 3"GSS2012: Deep Learning, Feature LearningNotes
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23. 23▶Geoffrey Hinton: "Some Applications of Deep Learning"GSS2012: Deep Learning, Feature LearningNotes
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24. 24▶Andrew Ng: "Non-linear Hypotheses, Pt. 2"GSS2012: Deep Learning, Feature LearningNotes
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25. 25▶Andrew Ng: "Non-linear Hypotheses, Pt. 1"GSS2012: Deep Learning, Feature LearningNotes
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26. 26▶Andrew Ng: "Advanced Topics + Research Philosophy / Neural Networks: Representation"GSS2012: Deep Learning, Feature LearningNotes
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