机器学习-李宏毅(2019)Machine Learning
1.P1ML Lecture 1_ Regression - Case Stud【学
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10.P10ML Lecture 5_ Logistic Regression【学it技术网xue.itjishu.cn】.mp4
11.P11ML Lecture 6_ Brief Introduction o【学it技术网xue.itjishu.cn】.mp4
12.P12ML Lecture 7_ Backpropagation【学it技术网xue.itjishu.cn】.mp4
13.P13Anomaly Detection (1_7)【学it技术网xue.itjishu.cn】.mp4
14.P13Anomaly Detection (2_7)【学it技术网xue.itjishu.cn】.mp4
15.P13Anomaly Detection (3_7)【学it技术网xue.itjishu.cn】.mp4
16.P13Anomaly Detection (4_7)【学it技术网xue.itjishu.cn】.mp4
17.P13Anomaly Detection (5_7)【学it技术网xue.itjishu.cn】.mp4
18.P13Anomaly Detection (6_7)【学it技术网xue.itjishu.cn】.mp4
19.P13Anomaly Detection (7_7)【学it技术网xue.itjishu.cn】.mp4
2.P2ML Lecture 3-1_ Gradient Descent【学it技术网xue.itjishu.cn】.mp4
20.P14ML Lecture 10_ Convolutional Neura【学it技术网xue.itjishu.cn】.mp4
21.P15ML Lecture 8-1_ “Hello world” of d【学it技术网xue.itjishu.cn】.mp4
22.P16ML Lecture 8-2_ Keras 2.0【学it技术网xue.itjishu.cn】.mp4
23.P17ML Lecture 8-3_ Keras Demo【学it技术网xue.itjishu.cn】.mp4
24.P18Attack ML Models (1_8)【学it技术网xue.itjishu.cn】.mp4
25.P18Attack ML Models (2_8)【学it技术网xue.itjishu.cn】.mp4
26.P18Attack ML Models (3_8)【学it技术网xue.itjishu.cn】.mp4
27.P18Attack ML Models (4_8)【学it技术网xue.itjishu.cn】.mp4
28.P18Attack ML Models (5_8)【学it技术网xue.itjishu.cn】.mp4
29.P18Attack ML Models (6_8)【学it技术网xue.itjishu.cn】.mp4
3.P3ML Lecture 3-2_ Gradient Descent 【学it技术网xue.itjishu.cn】.mp4
30.P18Attack ML Models (7_8)【学it技术网xue.itjishu.cn】.mp4
31.P18Attack ML Models (8_8)【学it技术网xue.itjishu.cn】.mp4
32.P19ML Lecture 9-1_ Tips for Training 【学it技术网xue.itjishu.cn】.mp4
33.P20ML Lecture 9-2_ Keras Demo 2【学it技术网xue.itjishu.cn】.mp4
34.P21ML Lecture 9-3_ Fizz Buzz in Tenso【学it技术网xue.itjishu.cn】.mp4
35.P22Explainable ML (2_8)【学it技术网xue.itjishu.cn】.mp4
36.P22Explainable ML (3_8)【学it技术网xue.itjishu.cn】.mp4
37.P22Explainable ML (4_8)【学it技术网xue.itjishu.cn】.mp4
38.P22Explainable ML (5_8)【学it技术网xue.itjishu.cn】.mp4
39.P22Explainable ML (6_8)【学it技术网xue.itjishu.cn】.mp4
4.P4ML Lecture 3-3_ Gradient Descent 【学it技术网xue.itjishu.cn】.mp4
40.P22Explainable ML (7_8)【学it技术网xue.itjishu.cn】.mp4
41.P22Explainable ML (8_8)【学it技术网xue.itjishu.cn】.mp4
42.P22ExplainableML【学it技术网xue.itjishu.cn】.mp4
43.P23ML Lecture 21-1_ Recurrent Neural【学it技术网xue.itjishu.cn】.mp4
44.P23ML Lecture 21-2_ Recurrent Neural【学it技术网xue.itjishu.cn】.mp4
45.P24Ordered (莊永松)【学it技术网xue.itjishu.cn】.mp4
46.P25ML Lecture 22_ Ensemble【学it技术网xue.itjishu.cn】.mp4
47.P28Life Long Learning (1_7)【学it技术网xue.itjishu.cn】.mp4
48.P29Sequence-to-sequence Learning【学it技术网xue.itjishu.cn】.mp4
49.P30Meta Learning – MAML (1_9)【学it技术网xue.itjishu.cn】.mp4
5.P5ML Lecture 1_ Regression - Demo【学it技术网xue.itjishu.cn】.mp4
50.P30Meta Learning – MAML (2_9)【学it技术网xue.itjishu.cn】.mp4
51.P30Meta Learning – MAML (3_9)【学it技术网xue.itjishu.cn】.mp4
52.P30Meta Learning – MAML (4_9)【学it技术网xue.itjishu.cn】.mp4
53.P30Meta Learning – MAML (5_9)【学it技术网xue.itjishu.cn】.mp4
54.P30Meta Learning – MAML (6_9)【学it技术网xue.itjishu.cn】.mp4
55.P30Meta Learning – MAML (7_9)【学it技术网xue.itjishu.cn】.mp4
56.P30Meta Learning – MAML (8_9)【学it技术网xue.itjishu.cn】.mp4
57.P30Meta Learning – MAML (9_9)【学it技术网xue.itjishu.cn】.mp4
58.P31ML Lecture 13_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
59.P32ML Lecture 14_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
6.P6ML Lecture 0-2_ Why we need to learn【学it技术网xue.itjishu.cn】.mp4
60.P33ML Lecture 15_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
61.P34Meta Learning - Gradient Descent a【学it技术网xue.itjishu.cn】.mp4
62.P34Meta Learning - Gradient Descent a【学it技术网xue.itjishu.cn】.mp4
63.P34Meta Learning - Gradient Descent a【学it技术网xue.itjishu.cn】.mp4
64.P35Meta Learning – Metric-based (1_3)【学it技术网xue.itjishu.cn】.mp4
65.P35Meta Learning – Metric-based (2_3)【学it技术网xue.itjishu.cn】.mp4
66.P35Meta Learning – Metric-based (3_3)【学it技术网xue.itjishu.cn】.mp4
67.P36ML Lecture 16_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
68.P37ML Lecture 17_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
69.P38ML Lecture 18_ Unsupervised Learni【学it技术网xue.itjishu.cn】.mp4
7.P7The Next Step for Machine Learning【学it技术网xue.itjishu.cn】.mp4
70.P39More about Auto-encoder (1_4)【学it技术网xue.itjishu.cn】.mp4
71.P39More about Auto-encoder (2_4)【学it技术网xue.itjishu.cn】.mp4
72.P39More about Auto-encoder (3_4)【学it技术网xue.itjishu.cn】.mp4
73.P39More about Auto-encoder (4_4)【学it技术网xue.itjishu.cn】.mp4
74.P40ML Lecture 23-1_ Deep Reinforcemen【学it技术网xue.itjishu.cn】.mp4
75.P41ML Lecture 23-2_ Policy Gradient 【学it技术网xue.itjishu.cn】.mp4
76.P42ML Lecture 23-3_ Reinforcement Lea【学it技术网xue.itjishu.cn】.mp4
77.P43Network Compression (1_6)【学it技术网xue.itjishu.cn】.mp4
78.P43Network Compression (2_6)【学it技术网xue.itjishu.cn】.mp4
79.P43Network Compression (3_6)【学it技术网xue.itjishu.cn】.mp4
8.P8ML Lecture 2_ Where does the error c【学it技术网xue.itjishu.cn】.mp4
80.P43Network Compression (4_6)【学it技术网xue.itjishu.cn】.mp4
81.P43Network Compression (5_6)【学it技术网xue.itjishu.cn】.mp4
82.P43Network Compression (6_6)【学it技术网xue.itjishu.cn】.mp4
83.P44GAN (Quick Review)【学it技术网xue.itjishu.cn】.mp4
84.P45Transformer【学it技术网xue.itjishu.cn】.mp4
85.P46ELMO, BERT, GPT【学it技术网xue.itjishu.cn】.mp4
86.P47Flow-based Generative Model【学it技术网xue.itjishu.cn】.mp4
9.P9ML Lecture 4_ Classification【学it技术网xue.itjishu.cn】.mp4