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Learning Networks
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Classroom Contents
Recent Developments in Over-parametrized Neural Networks, Part I
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- 1 Introduction
- 2 Outline
- 3 Goals
- 4 Whats Hard
- 5 Whats Easy
- 6 Non convex optimization
- 7 Notation
- 8 Overparametrization
- 9 Experiments
- 10 Rule of Thumb
- 11 Theoretical Problem
- 12 Optimal Optimization
- 13 Taxonomy of Results
- 14 Skip Connections
- 15 Expressivity
- 16 Geometric Results
- 17 Geometry Results
- 18 Onepoint convexity
- 19 Taylors theorem
- 20 Royer algorithm
- 21 Randomness
- 22 Theorem
- 23 Secondorder Stationary Points
- 24 Neural Networks
- 25 Optimality Conditions
- 26 Global Optimal
- 27 Other Losses
- 28 NonDegenerate Critical Points
- 29 Three Strategies
- 30 Second Order Descent
- 31 Stationary Points
- 32 Learning Networks
