[人工智能]李宏毅机器学习（2019）Machine Learning

李宏毅机器学习（2019）Machine Learning 资源介绍

《机器学习笔记2019》包括李宏毅对机器学习和深度学习的入门介绍，包括线性回归、逻辑回归、神经网络等基础知识，也包括从机器学习到深度学习的一个工程，并结合图像识别，详细讲解了机器学习在攻击与防护领域中的应用。

提到李宏毅老师，熟悉机器学习的读者应该都知道，他的机器学习视频被奉为领域内的经典视频之一，对于想要入门机器学习又想看中文讲解的人来说绝对是非常推荐的。而现在我们有更加完备的学习资料了，每节课程的内容、代码和中文笔记，供广大机器学习爱好者使用。

资源目录：

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

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