李宏毅2020机器学习深度学习
P1. Machine Learning 2020_ Course Introduction
P2. Rule of ML 2020
P3. Regression - Case Study
P4. Basic Concept
P5. Gradient Descent_1
P6. Gradient Descent_2
P7. Gradient Descent_3
P8. Optimization for Deep Learning 1_2 选学
P9. Optimization for Deep Learning 2_2 选学
P10. Classification_1
P11. Logistic Regression
P12. Brief Introduction of Deep Learning
P13. Backpropagation
P14. Tips for Training DNN
P15. Why Deep-
P16. PyTorch Tutorial
P17. Convolutional Neural Network
P18. Graph Neural Network 1_2 选学
P19. Graph Neural Network 2_2 选学
P20. Recurrent Neural Network Part I
P21. Recurrent Neural Network Part II
P22. Unsupervised Learning - Word Embedding
P23. Transformer
P24. Semi-supervised
P25. ELMO, BERT, GPT
P26. Explainable ML 1_8
P27. Explainable ML 2_8
P28. Explainable ML 3_8
P29. Explainable ML 4_8
P30. Explainable ML 5_8
P31. Explainable ML 6_8
P32. Explainable ML 7_8
P33. Explainable ML 8_8
P34. More about Explainable AI 选学
P35. Attack ML Models 1_8
P36. Attack ML Models 2_8
P37. Attack ML Models 3_8
P38. Attack ML Models 4_8
P39. Attack ML Models 5_8
P40. Attack ML Models 6_8
P41. Attack ML Models 7_8
P42. Attack ML Models 8_8
P43. More about Adversarial Attack 1_2 选学
P44. More about Adversarial Attack 2_2 选学
P45. Network Compression 1_6
P46. Network Compression 2_6
P47. Network Compression 3_6
P48. Network Compression 4_6
P49. Network Compression 5_6
P50. Network Compression 6_6
P51. Network Compression 1_2 - Knowledge Distillation .flv
P52. Network Compression 2_2 - Network Pruning 选学
P53. Conditional Generation by RNN & Attention
P54. Pointer Network
P55. Recursive
P56. Transformer and its variant 选学
P57. Unsupervised Learning - Linear Methods
P58. Unsupervised Learning - Neighbor Embedding
P59. Unsupervised Learning - Auto-encoder
P60. Unsupervised Learning - Deep Generative Model Part.flv
P61. Unsupervised Learning - Deep Generative Model Part.flv
P62. More about Auto-encoder 1_4
P63. More about Auto-encoder 2_4
P64. More about Auto-encoder 3_4
P65. More about Auto-encoder 4_4
P66. Self-supervised Learning 选学
P67. Anomaly Detection 1_7
P68. Anomaly Detection 2_7
P69. Anomaly Detection 3_7
P70. Anomaly Detection 4_7
P71. Anomaly Detection 5_7
P72. Anomaly Detection 6_7
P73. Anomaly Detection 7_7
P74. More about Anomaly Detection 选学
P75. Generative Adversarial Network1_10
P76. Generative Adversarial Network2_10
P77. Generative Adversarial Network3_10
P78. Generative Adversarial Network4_10
P79. Generative Adversarial Network5_10
P80. Generative Adversarial Network6_10
P81. Generative Adversarial Network7_10
P82. Generative Adversarial Network8_10
P83. Generative Adversarial Network9_10
P84. Generative Adversarial Network10_10
P85. SAGAN, BigGAN, SinGAN, GauGAN, GANILLA, NICE-GAN（选学.flv
P86. Transfer Learning
P87. More about Domain Adaptation 1_2 选学
P88. More about Domain Adaptation 2_2 选学
P89. Meta Learning – MAML 1_9
P90. Meta Learning – MAML 2_9
P91. Meta Learning – MAML 3_9
P92. Meta Learning – MAML 4_9
P93. Meta Learning – MAML 5_9
P94. Meta Learning – MAML 6_9
P95. Meta Learning – MAML 7_9
P96. Meta Learning – MAML 8_9
P97. Meta Learning – MAML 9_9
P98. More about Meta Learning 选学
P99. More about Meta Learning 选学
P100. Life Long Learning 1_7
P101. Life Long Learning 2_7
P102. Life Long Learning 3_7
P103. Life Long Learning 4_7
P104. Life Long Learning 5_7
P105. Life Long Learning 6_7
P106. Life Long Learning 7_7
P107. Deep Reinforcemen Learning3_1
P108. Deep Reinforcemen Learning3_2
P109. Deep Reinforcemen Learning3_3
P110. RL Advanced Version_1_Policy Gradient
P111. RL Advanced Version_2_ Proximal Policy Optimizatio.flv
P112. RL Advanced Version_3_Q-Learning
P113. RL Advanced Version_4_Q-Learning Advanced Tips
P114. RL Advanced Version_5_Q-Learning Continuous Action.flv
P115. RL Advanced Version_6_Actor-Critic
P116. RL Advanced Version_7_Sparse Reward
P117. RL Advanced Version_8_Imitation Learning
