Knowledge base
18 notes I keep while learning: written for my future self, open in case they help you. Formulas, code and worked examples included.
Statistics
Statistical modelling and machine learning, from confidence intervals to graph neural networks.
Linear algebra
The maths underneath the models: linear systems, bases, linear maps and eigenvalues.
Python & ML engineering
Working notes on Python, retrieval-augmented generation, LLMs and git.
SQL
How queries are executed, with exercises.
Research methods
Designing studies and questionnaires people can trust.