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Intro
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Classroom Contents
Deeply Learning Derivatives - From Hilbert to Riskfuel
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- 1 Intro
- 2 Quadratic Equations
- 3 Nomograms
- 4 Hilberts 13th Problem
- 5 Multivariate Continuous Functions
- 6 Riskfuel
- 7 Deep neural nets
- 8 Meme
- 9 Theory
- 10 Deep Neural Theory
- 11 What Matters
- 12 Frequency Principle
- 13 Quantity beats quality
- 14 Data placement
- 15 Exotic options
- 16 Bermuda swaption
- 17 Double Knockout Partial Barrier Option
- 18 Three Pillars of Riskfuel
- 19 Pricer Riskfuel
- 20 Retraining
- 21 Putting data where it needs to go
- 22 More layers or more neurons
- 23 Double knockout pairs
- 24 Black shells
