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_

深度学习源代码，hinton论文附带源代码，包括图像编码和解码两部分。通过训练深层网络降维高位图片数据，并比较复原误差。主要利用级联Boltzmann估计多层网络初始参数，使得多层神经网络可以被很好的训练并得到理想结果。

官网下载太慢，可以在这里下载。python版本的numpyy，官方下载特别慢，所以放到csdn上供大家下载