In Post 2 we built softmax regression from scratch, in Post 13 we let autograd compute our gradients, and in Post 14 we added regularization to keep the weights honest. Every one of those posts quietly assumed the same thing: that once we had a gradient, we knew what to
Post 15 taught us to find structure in unlabeled tabular data by clustering rows that look alike. This post goes after a different kind of unlabeled structure, the kind that lives between rows rather than inside them: what gets bought together. There is no target column, no train and test
In post 15 we built the digits loading and inspection pipeline, and we ended with a matrix of 60,000 images, each one a 28 by 28 grid of pixels. Flatten that grid and every image becomes a point in 784 dimensions. This post takes those points and asks a
In the previous posts we built classifiers that needed labels for every image, but most data arrives without a teacher. This post turns that assumption on its head: we hand 70,000 handwritten digits to a collection of unsupervised algorithms and ask them to find structure on their own. By
In Posts 11 through 13 we built classifiers that draw boundaries directly, fitting logistic curves and trees to the Adult Census Income data. This post takes the opposite route and then the long way around: we start with generative classifiers that model how each income class produces its features, pivot
In the last few posts we built up the machinery of supervised learning from first principles, from nearest neighbors through decision trees and ensemble methods. Now we turn to the oldest and most enduring family in the toolbox: linear models. By the end of this post we will have fitted
In Post 11 we learned to interpret our models and engineer features, but we left a nagging question on the table: how do we know the numbers we report are trustworthy? This post answers that question with the Adult Census Income dataset, and by the end we land a random
In Post 10 we learned how to split data into training, validation, and test sets, and in Posts 3 and 4 we built our first regression models and baselines. Now we take the next step: training our first real classifiers on the UCI Adult Census Income dataset, where we predict
In the last few posts we built regression models from raw features and learned how to inspect what drives them, but every one of those models assumed a table that was already tidy. This post takes the opposite starting point: we begin with the raw New York City Airbnb listings,
In Post 8 we built the optimization toolkit that turns a problem into a solvable form, learning how gradients and convexity give us a path to good answers. Now we face the harder truth: some problems resist every clever trick we know, and the only honest response is to understand
In Post 6 we learned to clean tabular data until it told a coherent story. This post takes that same discipline and applies it to a different kind of structure: the directed, weighted graph of the world's flight network. We build a graph from raw OpenFlights data, then
In Post 6 we built recursion and divide-and-conquer from first principles, learning to split problems into independent halves and combine the answers. This post takes those tools and points them at a harder class of problems: ones where the subproblems overlap, where strings refuse to be split cleanly,