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1. 1▶Lecture 12.1 - Linear AlgebraAlelab AlelabNotes
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2. 2▶Lecture 12.7 - Perturbation ModelsAlelab AlelabNotes
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3. 3▶Lecture 12.4 - Generators, Shift Operators and Frequency RepresentationsAlelab AlelabNotes
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4. 4▶Lecture 12.8 - Stability TheoremsAlelab AlelabNotes
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5. 5▶Lecture 12.9 - Spectral RepresentationsAlelab AlelabNotes
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6. 6▶Lecture 12.5 - Convolutional Information ProcessingAlelab AlelabNotes
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7. 7▶Lecture 12.6 - Algebraic Neural NetworksAlelab AlelabNotes
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8. 8▶Lecture 12.2 - Algebraic Signal ProcessingAlelab AlelabNotes
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9. 9▶Lecture 12.3 - Polynomials in an Algebra and Polynomial FunctionsAlelab AlelabNotes
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10. 10▶Lecture 11.7 - Epidemic Modeling with GRNNsAlelab AlelabNotes
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11. 11▶Lecture 13 | (2/5) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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12. 12▶Lecture 12 | (1/5) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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13. 13▶Lecture 11 | (3/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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14. 14▶Lecture 10 | (2/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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15. 15▶Lecture 8 | Batch Normalization, Dropout and other Regularization methods(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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16. 16▶Lecture 9 | (1/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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17. 17▶Lecture 7 | Acceleration, Regularization, and Normalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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18. 18▶Lecture 6 | Convergence, Loss Surfaces, and Optimization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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19. 19▶The Neural Basis of Vision Through Convolutional Neural Networks by Mike Tarr(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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20. 20▶Lecture 5 | Convergence, Learning Rates, and Gradient Descent(Old) Lecture Series | Introduction to Deep Learning, 11-785, Fall 2019Notes
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21. 21▶(Old) Lecture 9 | (2/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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22. 22▶(Old) Lecture 15 | (3/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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23. 23▶Continual Learning in Neural Networks by Pulkit Agarwal(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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24. 24▶(Old) Lecture 11 | (1/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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25. 25▶Cascade-Correlation and Deep Learning by Scott Fahlman (Spring 2019)(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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26. 26▶(Old) Lecture 12 | (2/3) Recurrent Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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27. 27▶(Old) Lecture 10 | (3/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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28. 28▶(Old) Lecture 8 | (1/3) Convolutional Neural Networks(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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29. 29▶(Old) Lecture 7 | Optimization and Generalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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30. 30▶(Old) Lecture 6 | Acceleration, Regularization, and Normalization(Old) Lecture Series | Introduction to Deep Learning, 11-785, Spring 2019Notes
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31. 31▶F18 Lecture 11: Recurrent Neural Networks (RNNs) (Part 2)(Old) 11-785, Fall 2018Notes
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32. 32▶F18 Lecture 12: Loss functions and sequence prediction for RNNs(Old) 11-785, Fall 2018Notes
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33. 33▶F18 Lecture 10: Recurrent Neural Networks (RNNs) (Part 1)(Old) 11-785, Fall 2018Notes
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34. 34▶F18 Lecture 9: Convolutional Neural Networks (Part 2)(Old) 11-785, Fall 2018Notes
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35. 35▶F18 Lecture 8: Convolutional Neural Networks (Part 1)(Old) 11-785, Fall 2018Notes
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36. 36▶F18 Lecture 7: Optimization Part 2(Old) 11-785, Fall 2018Notes
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37. 37▶F18 Lecture 6: Optimization Part 1(Old) 11-785, Fall 2018Notes
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38. 38▶F18 Lecture 3: Neural Network Training(Old) 11-785, Fall 2018Notes
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39. 39▶F18 Lecture 5 : Backpropagation (cont.)(Old) 11-785, Fall 2018Notes
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40. 40▶F18 Lecture 4 : Backpropagation(Old) 11-785, Fall 2018Notes
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41. 41▶D3L5 Parametric Speech Synthesis (by Antonio Bonafonte)Deep Learning for Speech and Language 2017Notes
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42. 42▶D3L2 Speech Recognition with Deep Networks (by José A. R. Fonollosa)Deep Learning for Speech and Language 2017Notes
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43. 43▶D3L3 Speaker Identification I (by Javier Hernando)Deep Learning for Speech and Language 2017Notes
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44. 44▶D3L4 Neural Machine Translation (by Marta Ruiz Costa-jussà)Deep Learning for Speech and Language 2017Notes
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45. 45▶D3L1 Language Model (by Marta Ruiz Costa-jussà)Deep Learning for Speech and Language 2017Notes
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46. 46▶D2L6 Advanced Deep Architectures (by Xavier Giró)Deep Learning for Speech and Language 2017Notes
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47. 47▶D2L4 Word Embeddings - Word2Vec (by Antonio Bonafonte)Deep Learning for Speech and Language 2017Notes
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48. 48▶D2L5 Generative Adversarial Networks (by Santiago Pascual)Deep Learning for Speech and Language 2017Notes
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49. 49▶D2L2 Recurrent Neural Networks I (by Santiago Pascual)Deep Learning for Speech and Language 2017Notes
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50. 50▶D2L3 Recurrent Neural Networks II (by Santiago Pascual)Deep Learning for Speech and Language 2017Notes
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