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README.md

@@ -69,5 +69,8 @@ ppt/pdf支持直链下载。
 |Lecture 3|[卷积神经网络CNN](https://www.bilibili.com/video/BV1Wv411h7kN?p=31)|Video:<br/>[为什么用了验证集还是过拟合](https://www.bilibili.com/video/BV1Wv411h7kN?p=32)<br/>[鱼与熊掌可以兼得的机器学习](https://www.bilibili.com/video/BV1Wv411h7kN?p=33)<br/><br/>PDF:<br/>[Validation](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/validation.pdf)<br/>[Why Deep](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/whydeep%20(v3).pdf)|[Spatial Transformer Layer](https://www.bilibili.com/video/BV1Wv411h7kN?p=34)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=35)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Machine%20Learning%20HW3%20-%20Image%20Classification.pdf)<br/>[Code](https://colab.research.google.com/drive/15hMu9YiYjE_6HY99UXon2vKGk2KwugWu)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw3b)|
 |Lecture 4|[自注意力机制(Self-attention)（上）](https://www.bilibili.com/video/BV1Wv411h7kN?p=41)<br/>[自注意力机制(Self-attention)（下）](https://www.bilibili.com/video/BV1Wv411h7kN?p=42)|Video:<br/>[None]<br/><br/>PDF:<br/>[None]|[RNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=40)<br/>[RNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=41)<br/>[GNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=42)<br/>[GNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=43)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=45)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Machine%20Learning%20HW4.pdf)<br/>[Code](https://colab.research.google.com/drive/1gC2Gojv9ov9MUQ1a1WDpVBD6FOcLZsog?usp=sharing)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw4)|
 |Lecture 5|[类神经网络训练不起来怎么办（五）批次标准化](https://www.bilibili.com/video/BV1Wv411h7kN?p=48)<br/>[Transformer（上）](https://www.bilibili.com/video/BV1Wv411h7kN?p=49)<br/>[Transformer（下）](https://www.bilibili.com/video/BV1Wv411h7kN?p=50)|Video:<br/>[各式各样神奇的自注意力机制 (Self-attention) 变型](https://www.bilibili.com/video/BV1Wv411h7kN?p=51)<br/><br/>PDF:<br/>[xformer](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/xformer%20(v8).pdf)|[NAT model](https://www.bilibili.com/video/BV1Wv411h7kN?p=52)<br/>[Pointer network](https://www.bilibili.com/video/BV1Wv411h7kN?p=53)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=54)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/HW05.pdf)<br/>[Code](https://colab.research.google.com/drive/1Tlyk2vCBQ8ZCuDQcCSEWTLzr1_xYF9CL#scrollTo=Le4RFWXxjmm0)<br/>[Submission](https://ml.ee.ntu.edu.tw/hw5/)|
+|Lecture 6|[GAN（一）基本概念介绍](https://www.bilibili.com/video/BV1Wv411h7kN?p=58)<br/>[GAN（二）理论介绍与WGAN](https://www.bilibili.com/video/BV1Wv411h7kN?p=59)<br/>[GAN（三）生成器效能评估与条件式生成](https://www.bilibili.com/video/BV1Wv411h7kN?p=60)<br/>[GAN（四）Cycle GAN](https://www.bilibili.com/video/BV1Wv411h7kN?p=61)|Video:<br/>[None]<br/><br/>PDF:<br/>[None]|[Theory of GAN (part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=62)<br/>[Theory of GAN (part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=63)<br/>[Theory of GAN (part 3)](https://www.bilibili.com/video/BV1Wv411h7kN?p=64)<br/>[Deep Generative Model (part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=65)<br/>[Deep Generative Model (part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=66)<br/>[FLOW-based Model](https://www.bilibili.com/video/BV1Wv411h7kN?p=67)|[Video]<br/>[Slide]<br/>[Code]<br/>|
+|Lecture 7|[自监督学习（一）芝麻街与进击的巨人](https://www.bilibili.com/video/BV1Wv411h7kN?p=70)<br/>[自监督学习（二）BERT简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=71)<br/>[自监督学习（三）BERT的奇闻轶事](https://www.bilibili.com/video/BV1Wv411h7kN?p=72)<br/>[自监督学习（四）GPT的野望](https://www.bilibili.com/video/BV1Wv411h7kN?p=73)|Video:<br/>[如何有效的使用自督导式模型](https://www.bilibili.com/video/BV1Wv411h7kN?p=74)<br/><br/>PDF:<br/>[Recent Advance of Self-supervied learning for NLP](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/PLM.pdf)|[BERT (part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=75)<br/>[BERT (part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=76)<br/>[BERT(part 3)](https://www.bilibili.com/video/BV1Wv411h7kN?p=77)|[Video]<br/>[Slide]<br/>[Code]<br/>|
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