-
Document 1
-
AI101 DocumentLecture1.1
-
-
Theory 17
-
1. IntroductionLecture2.140 min
-
2. Linear Regression : Theory : What is Linear Regression ?Lecture2.206 min
-
3. Linear Regression : Example : Example 2 & Example 3Lecture2.318 min
-
3. Linear Regression : Theory : StateLecture2.407 min
-
4. Linear Regression : Theory : How to create model Part 1Lecture2.530 min
-
5. Linear Regression : Theory : How to create model Part 2Lecture2.612 min
-
6. Linear Regression : Theory : How to create modelLecture2.736 min
-
7. Linear Regression : Theory : When will we use Linear Regression ?Lecture2.832 min
-
8. ปัญหาเชาว์ : หลอดไฟ 1000 หลอด กับคน 1000 คนLecture2.910 min
-
8. Linear Regression : Code : Import LibraryLecture2.1010 min
-
9. Linear Regression : Code : Write Function Part 1Lecture2.1123 min
-
10. Linear Regression : Code : Write Function Part 2Lecture2.1227 min
-
11. Linear Regression : Code : Write Function Part 3Lecture2.1325 min
-
12. Linear Regression : Code : Read Data and Prepare Data and ExampleLecture2.1415 min
-
13. Linear Regression : Code : Create Model and ExampleLecture2.1517 min
-
14. Linear Regression : Code : Making Prediction and Example 1Lecture2.1617 min
-
15. Linear Regression : Example : Example 2 & Example 3Lecture2.1718 min
-
-
Part2: Multiple Regression 18
-
16. การคูณ MatrixLecture3.128 min
-
17. Transpose MatrixLecture3.205 min
-
18. Matrix_การ Invert MaterixLecture3.337 min
-
19. Multiple Regression : Theory : What is Multiple Regression ?Lecture3.408 min
-
20. Multiple Regression : Theory : StateLecture3.523 min
-
21. Multiple Regression : Theory : How to create model Part 1Lecture3.626 min
-
22. Multiple Regression : Theory : How to create model Part 2Lecture3.725 min
-
23. Multiple Regression : Theory : When will we use MultipleLecture3.804 min
-
24. Multiple Regression : Theory : ConclusionLecture3.902 min
-
25. Multiple Regression : ตัวอย่างการคำนวณ W แบบ Global Part 1Lecture3.1022 min
-
26. Multiple Regression : ตัวอย่างการคำนวณ W แบบ Global Part 2Lecture3.1140 min
-
27. Multiple Regression : Code : Import LibraryLecture3.1209 min
-
28. Multiple Regression : Code : Write FunctionLecture3.1319 min
-
29. Multiple Regression : Code : Read Data & Prepare Data & Example 1Lecture3.1424 min
-
30. Multiple Regression : Code : Create Model & Example 1Lecture3.1510 min
-
31. Multiple Regression : Code : Making Prediction & Example 1 & Example 2Lecture3.1610 min
-
32. Multiple Regression : Example : Example 3 – Example 517Lecture3.1716 min
-
33. Multiple Regression : การเตรียม Dataset เพื่อใช้พยากรณ์ SET50 & วิธีการหา Research PaperLecture3.1833 min
-
-
Part3: Multiple Regression : Theory 15
-
34. Calculus : Chain RuleLecture4.108 min
-
35. Multiple Regression : Theory : How to create model (Local) Part 1Lecture4.222 min
-
36. Multiple Regression : Theory : How to create model (Local) Part 2Lecture4.315 min
-
37. Multiple Regression : Theory : How to create model (Local) Part 3Lecture4.420 min
-
38. Multiple Regression : Theory : How to create model (Local) Part 4Lecture4.512 min
-
39. Multiple Regression : Theory : ตัวอย่างการคำนวณ W แบบ Local Part 1Lecture4.623 min
-
40. Multiple Regression : Theory : ตัวอย่างการคำนวณ W แบบ Local Part 2Lecture4.722 min
-
41. Multiple Regression : Theory : ตัวอย่างการคำนวณ W แบบ Local Part 3Lecture4.820 min
-
42. Multiple Regression : Code (Local) & Example (Local)Lecture4.926 min
-
43. Normalization : Rescaling (Min-Max Normalization)Lecture4.1037 min
-
44. Normalization : Mean NormalizationLecture4.1114 min
-
45. Normalization : StandardizationLecture4.1228 min
-
46. Normalization : Scaling to Unit LengthLecture4.1310 min
-
47. Normalization : CodeLecture4.1422 min
-
48. Multiple Regression : Example (Local + Normalization)Lecture4.1532 min
-
-
Part4: Logistic Regression : Theory 18
-
49. Logistic Regression : IntroductionLecture5.115 min
-
50. Logistic Regression : Theory : What is Logistic Regression ?Lecture5.218 min
-
51. Logistic Regression : Theory : State (Binary Class)Lecture5.318 min
-
52. Logistic Regression : Theory : How to create model (Binary Class)Lecture5.418 min
-
53. Logistic Regression : Theory : ตัวอย่างการคำนวณ W (Binary Class) Part 1Lecture5.521 min
-
54. Logistic Regression : Theory : ตัวอย่างการคำนวณ W (Binary Class) Part 2Lecture5.637 min
-
55. Logistic Regression : Code (Binary Class)Lecture5.711 min
-
56. Logistic Regression : Example : อธิบาย Example 1Lecture5.814 min
-
57. Logistic Regression : Example : Example 1 – Example 3Lecture5.918 min
-
58. Logistic Regression : Introduction (Multiclass)Lecture5.1008 min
-
59. Logistic Regression : Theory : State (Multiclass)Lecture5.1131 min
-
60. Logistic Regression : Theory : How to create model (Multiclass)Lecture5.1218 min
-
61. Logistic Regression : Theory : ตัวอย่างการคำนวณ W (Multiclass)Lecture5.1322 min
-
62. Logistic Regression (Multiclass) : Theory : What is Logistic Regression ?Lecture5.1404 min
-
63. Logistic Regression (Multiclass) : Theory : StateLecture5.1527 min
-
64. Logistic Regression (Multiclass) : Theory : How to create modelLecture5.1640 min
-
65. Logistic Regression (Multiclass) : CodeLecture5.1733 min
-
66. Logistic Regression (Multiclass) : ExampleLecture5.1826 min
-
-
Part5: Neural Network : Theory 19
-
67. Neural Network : IntroductionLecture6.113 min
-
68. Neural Network : Theory : What is Neural Network ? & StateLecture6.233 min
-
69. Neural Network : Theory : Activation FunctionLecture6.311 min
-
70. Neural Network : Theory : ตัวอย่างการคำนวณ Feed ForwardLecture6.434 min
-
71. Neural Network : Theory : How to create model Part 1Lecture6.538 min
-
72. Neural Network : Theory : How to create model Part 2Lecture6.644 min
-
73. Neural Network : Theory : ตัวอย่างการคำนวณ Feed Forward (Version ละเอียด)Lecture6.726 min
-
74. Neural Network : Theory : How to create model for Regression Part 1 (Version ละเอียด)Lecture6.831 min
-
75. Neural Network : Theory : How to create model for Regression Part 2 (Version ละเอียด)Lecture6.943 min
-
76. Neural Network : Theory : How to create model for Regression Part 3 (Version ละเอียด)Lecture6.1037 min
-
77. Neural Network : Theory : How to create model for Regression Part 4 (Version ละเอียด)Lecture6.1108 min
-
78. Neural Network : Code : Regression Part 1Lecture6.1230 min
-
79. Neural Network : Code : Regression Part 2Lecture6.1332 min
-
80. Neural Network : Code : Regression Part 3Lecture6.1431 min
-
81. Neural Network : Code : Regression Part 4Lecture6.1512 min
-
82. Neural Network : Example : RegressionLecture6.1620 min
-
83. Neural Network : Theory : How to create model for ClassificationLecture6.1714 min
-
84. Neural Network : Code : ClassificationLecture6.1821 min
-
85. Neural Network : Example : ClassificationLecture6.1920 min
-
-
Part6: conclusions : Theory 12
-
86. Improvement : สรุปเนื้อหา Part 1Lecture7.140 min
-
87. Improvement : สรุปเนื้อหา Part 2Lecture7.228 min
-
88. Improvement : Speed : วิธีคำนวณ CostLecture7.326 min
-
89. Improvement : Speed : Batch SizeLecture7.415 min
-
90. Improvement : Speed : Code & ExampleLecture7.516 min
-
91. Improvement : Precision : L1, L2, Elastic Net (Idea)Lecture7.640 min
-
92. Improvement : Precision : Dropout Regularization (Idea)Lecture7.716 min
-
93. Improvement : Precision : L1, L2, Elastic Net (Detail) & CodeLecture7.816 min
-
94. Improvement : Precision : Dropout Regularization (Detail) & CodeLecture7.931 min
-
95. Course Conclusion : All Code of AI101Lecture7.1022 min
-
96. Inspiration ในการเปิดคอร์สที่มีทั้ง Theory, Code, ExampleLecture7.1107 min
-
97. ความฝันของผมLecture7.1213 min
-
This content is protected, please login and enroll course to view this content!