Notice: These are my personal notes on topics that sparked my interest. This project may not be actively maintained in the future. There is no guarantee of the accuracy or correctness of the information contained herein. I welcome bug reports and pull requests; however, I make no guarantees regarding fixes or responses.

Tutorials & notes

1. Derivation of the Expectation–Maximization (EM) Algorithm for Gaussian Mixture Models (GMM)
2. Derivation of the Posteriors for Thompson Sampling
3. Thoughts on the Vector Quantized Variational Autoencoder
4. Peter-Clark Algorithm and conditional independence tests
5. Sparse Auto-Encoder for bio-embeddings
6. Notes on Doubly Robust Learner (DRLearner)
7. Physics-Informed Neural Networks