# Notes with code

## Notes on Probabilistic Models of Cognition

- Chapter 3: Conditioning
- Chapter 4: Causal & Statistical Dependence
- Chapter 5: Conditional Dependence
- Chapter 6: Bayesian Data Analysis
- Chapter 7: Algorithms for Inference
- Chapter 9: Learning as Conditional Inference
- Chapter 10: Learning with a Language of Thought
- Chapter 11: Hierarchical Models
- Chapter 12: Occamâ€™s Razor
- Chapter 13: Learning Deep Continuous Functions
- Chapter 14: Mixture Models
- Chapter 15: Social Cognition

## Notes on `Self-supervised Learning`

- Contrastive Predictive Coding
- MoCo
- Contrastive Multiview Coding
- Deep InfoMax
- SimCLR
- Invariant Information Clustering
- On Mutual Information Maximization for Representation Learning

## Notes on Visual Transformers

- An Image is worth 16x16 words
- DEIT: Data Efficient Image Transformers
- DiNo: self-
`DI`

stillation with`NO`

labels - BeIT: Bert Encoding representations for Image Transformers