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Document 1
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Deep LearningLecture1.1
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Part1: Basic Knowledge 6
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1. AI : IntroductionLecture2.101 hour 02 min
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2. Basic Knowledge-DerivativeLecture2.215 min
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3. Basic Knowledge-Chain RuleLecture2.312 min
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4. Basic Knowledge-MatrixLecture2.418 min
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5. Basic Knowledge-PythonLecture2.501 hour 06 min
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6. Basic Knowledge-NumpyLecture2.637 min
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Part2: Theory: Multiple Regression 11
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7. Multiple Regression : Theory : What is Multiple Regression ? & StateLecture3.101 hour 01 min
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8. Multiple Regression : Theory : How to create model (Global)Lecture3.227 min
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9. Multiple Regression : Theory : Proof GlobalLecture3.342 min
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10. Multiple Regression : Theory : When will we use Multiple Regression ? & Code & ExampleLecture3.401 hour 12 min
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11. Multiple Regression : Theory : รีวิวปัญหาของ GlobalLecture3.537 min
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12. Multiple Regression : Theory : How to create model (Local)Lecture3.627 min
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13. Multiple Regression : Theory : Proof LocalLecture3.721 min
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14. Multiple Regression : Code & ExampleLecture3.830 min
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15. NormalizationLecture3.952 min
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16. Multiple Regression : Example + NormalizationLecture3.1002 min
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17. Multiple Regression : Theory : Error SurfaceLecture3.1115 min
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Part3: Logistic Regression 3
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18. Logistic Regression : Theory : What is Logistic Regression ?Lecture4.120 min
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19. Logistic Regression : Theory : State & How to create modelLecture4.220 min
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20. Logistic Regression : Code & ExampleLecture4.340 min
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Part3: Multinomial Logistic Regression 9
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21. Multinomial Logistic Regression : Theory : What is Multinomial Logistic Regression ?Lecture5.130 min
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22. Multinomial Logistic Regression : Theory : StateLecture5.231 min
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23. Multinomial Logistic Regression : Theory : How to create model & ProofLecture5.310 min
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24. Multinomial Logistic Regression : Code & ExampleLecture5.410 min
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25. Multiple Regression : Theory : ตัวอย่างการคำนวณ Multiple Regression (Global)Lecture5.520 min
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26. Multiple Regression : Theory : ตัวอย่างการคำนวณ Multiple Regression (Local)Lecture5.638 min
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27. Multinomial Logistic Regression : Theory : ตัวอย่างการคำนวณ Multinomial Logistic RegressionLecture5.731 min
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28. Multinomial Logistic Regression : Theory : ตัวอย่างการคำนวณ MLoRLecture5.820 min
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29. Multinomial Logistic Regression : Theory : Workshop-TheoryLecture5.901 hour 02 min
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Part4: Neural Network 17
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30. Neural Network : Theory : What is Neural NetworkLecture6.140 min
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31. Neural Network : Theory : StateLecture6.240 min
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32. Neural Network : Theory : Activation FunctionLecture6.301 hour 06 min
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33. Neural Network : Theory : How to create model & When will we use Neural NetworkLecture6.414 min
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34. Neural Network : Theory : ตัวอย่างการคำนวณ Neural Network (Forward)Lecture6.501 hour
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35. Neural Network : Theory : AUTODIFF Part1Lecture6.647 min
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36. Neural Network : Theory : AUTODIFF Part2Lecture6.701 hour
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37. Neural Network : Theory : ภาพที่เกิดขึ้นใน Neural NetworkLecture6.801 hour 03 min
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38. Neural Network : Code : AUTODIFF : Overloading FunctionLecture6.932 min
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39. Neural Network : Code : AUTODIFF : เก็บข้อมูลLecture6.1056 min
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40. Neural Network : Code : AUTODIFF : backward and gradLecture6.1101 hour
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41. Neural Network : Code : NUMPY AUTODIFF Part 1Lecture6.1238 min
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42. Neural Network : Code : NUMPY AUTODIFF Part 2Lecture6.1340 min
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43. Neural Network : Code : NUMPY AUTODIFF Part 3Lecture6.1405 min
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44. Neural Network : Example : Data Preparation for Financial Machine LearningLecture6.1536 min
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45. Neural Network : Example : Labeling for Financial Machine LearningLecture6.1648 min
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46. Neural Network : Example : WorkshopLecture6.1725 min
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Part5: RNN 13
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47. RNN : Theory : What is Recurrent Neural Network ?Lecture7.101 hour
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48. RNN : Theory : When will we use RNN ?Lecture7.233 min
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49. RNN : Theory : ตัวอย่างการคำนวณ Vanilla RNNLecture7.350 min
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50. RNN : Theory : Bag of WordsLecture7.440 min
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51. RNN : OverviewLecture7.511 min
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52. RNN : Theory : LSTM Part 1Lecture7.622 min
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53. RNN : Theory : LSTM Part 2Lecture7.735 min
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54. RNN : Theory : LSTM Part 3****Lecture7.8
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55. RNN : Theory : GRUsLecture7.940 min
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56. RNN : Theory : Word2VecLecture7.1054 min
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57. RNN : Exercise : Workshop – Bag of WordsLecture7.1101 hour 08 min
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58. RNN : วิธีการ Feed Time Series DataLecture7.1240 min
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59. RNN : Exercise : Workshop – Time SeriesLecture7.1301 hour
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Part6: CNN 9
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60. CNN : OverviewLecture8.110 min
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61. CNN : Theory Part 1Lecture8.240 min
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62. CNN : Theory Part 2Lecture8.350 min
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63. CNN : Theory Part 3Lecture8.441 min
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64. CNN : Example Part 1Lecture8.524 min
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65. CNN : Example Part 2Lecture8.640 min
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66. Time Series RepresentationLecture8.754 min
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67. Neural Network : Improvement : GeneralizationLecture8.833 min
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68. Neural Network : Improvement : SpeedLecture8.956 min
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