15. Learning (Deep) Continuous Functions
Probabilistic Models of Cognition
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1. Introduction
2. Generative Models
Exercises
3. Conditioning
Exercises
4. Causal and Statistical Dependence
Exercises
5. Conditional Dependence
Exercises
6. Social Cognition
Exercises
7. Interlude: Bayesian Data Analysis
Exercises
8. Interlude: Algorithms for Inference
9. Rational Process Models
10. Learning as Conditional Inference
11. Learning with a Language of Thought
12. Hierarchical Models
13. Occam’s Razor
14. Mixture Models
15. Learning (Deep) Continuous Functions
16. Appendix – JavaScript Basics
17. Appendix – Useful Distributions
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Chapter 15: Learning (Deep) Continuous Functions
14. Mixture Models
16. Appendix – JavaScript Basics