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Introduction
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Classroom Contents
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
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- 1 Introduction
- 2 Explainable Machine Learning
- 3 Problems with Taylor Decomposition
- 4 Alternative Explanation Techniques
- 5 Four Properties of Good Explanation Techniques
- 6 LRP Rules for Deep Rectifier Networks
- 7 LRP Rules: LRP-O
- 8 Implementing LRP Efficiently
- 9 LRP as a Deep Taylor Decomposition (ii)
- 10 Properties of Explanations (ii)
- 11 Rule Choices with VGG-16
- 12 Conclusion
- 13 Against
- 14 Questions?
