Stochastic

In Post 14 we built the baselines that every forecast has to beat, and in Post 13 we learned to read autocorrelation and lag features. Now we point both at a real meter. This post takes four years of minute-by-minute household electricity readings and tur

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,

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 n

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 s

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

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

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

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

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 datase

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 Yor