Stochastic Blog
In the last post we wrangled the Adult Census Income dataset into a clean, analysis-ready form. Now we turn that clean data into a laboratory for the core ideas of probability and estimation: Bayes' theorem, the distributions that describe real populations, the limit theorems that justify our confidence,
Stochastic Blog
Last time we built our first Python pipeline and learned to load, inspect, and manipulate data. Now we take the next step: every machine learning model, from linear regression to deep neural networks, rests on three pillars: linear algebra, calculus, and convex optimization. We'll build each one from
Stochastic Blog
Every machine learning project, no matter how sophisticated, rests on a handful of foundational ideas. In this post we build them from scratch using the UCI Adult Census Income dataset, a classic binary classification task where we predict whether someone earns more than $50,000 per year. By the time