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Conclusions & Impications
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Classroom Contents
Understanding Deep Learning Requires Rethinking Generalization
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- 1 Intro
- 2 Presentation Outline
- 3 Universal Approximation Theorem
- 4 L2 Regularization - "Weight Decay"
- 5 Dropout
- 6 Data Augmentation
- 7 Rondomization Tests
- 8 Results of Randomization Tests
- 9 Conclusions & Impications
- 10 Explicit Regularization Tests
- 11 Implicit Regularization Findings
- 12 Finite-Sample Expressivity of Neural Networks
- 13 Appeal to Linear Models
- 14 Investigating SGD
- 15 Final Conclusions
