018 Interpretability of Neural Networks (Lecture 7)

時間 2021-01-02

標籤 CS230 神經網絡深度學習简体版

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目錄 Outline I.A. Interpreting and visualizing Neural Networks with sliency maps I.B. Interpreting and visualizing Neural Networks with occusion sensitivity III.C. Interpreting NNs using class activatio

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1. [cv231n] Lecture 7 | Training Neural Networks II
2. [cs224n] Lecture 3 – Neural Networks
3. The Building Blocks of Interpretability
4. cs231n-notes-Lecture-10：Recurrent Neural Networks
5. CS231n lecture 5 Convolutional Neural Networks(CNN)
6. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
7. ICLR2014_Intriguing properties of neural networks
8. Applications of Graph Neural Networks
9. Lecture 18: Tree Recursive Neural Networks, Constituency Parsing, and Sentiment
10. Lecture 6: Language Models and Recurrent Neural Networks
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1. [cv231n] Lecture 7 | Training Neural Networks II
2. [cs224n] Lecture 3 – Neural Networks
3. The Building Blocks of Interpretability
4. cs231n-notes-Lecture-10：Recurrent Neural Networks
5. CS231n lecture 5 Convolutional Neural Networks(CNN)
6. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
7. ICLR2014_Intriguing properties of neural networks
8. Applications of Graph Neural Networks
9. Lecture 18: Tree Recursive Neural Networks, Constituency Parsing, and Sentiment
10. Lecture 6: Language Models and Recurrent Neural Networks

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