ml

Machine learning experiments, from-scratch implementations, coursework, and notes.

Layout

Directory	Contents
`implementations/`	Small from-scratch implementations of ML algorithms and primitives
`experiments/`	Self-contained Kaggle entries and side projects
`fundamentals/`	Homework and worked exercises from ML courses (TMU + Kaggle Learn)
`notes/`	Topic notes and cheatsheets
`paper-notes/`	Paper notes (in progress)

Each subdirectory has its own README.

Polished standalone projects live in their own repositories:

speculative-decoding - speculative decoding on Apple M2 Max in PyTorch+MPS and MLX
hft-jump-diffusion - optimal execution under Semi-Markov and Hawkes jump-diffusion models

MIT, see LICENSE.