Indexing 150+ academic notes, code snippets, and cheat sheets from my Data Science curriculum. Updated weekly.
Detailed breakdown of Bayes Theorem, conditional probability, and prior/posterior distributions with Python examples.
Deep dive into Object Oriented Programming in Python. Covers polymorphism, encapsulation, and magic methods.
Understanding vectors, matrices, determinants, and how to calculate eigenvalues for dimensionality reduction.
A practical guide to creating Dockerfiles for Python/Scikit-Learn applications and deploying them.
Notes on partial derivatives and applying the chain rule in higher dimensions for gradient descent optimization.
Implementing line-of-sight and shooting mechanics using Physics.Raycast in C#. Includes code snippets.