In the last few posts we turned raw text into clean tokens, then into bags of words and TF-IDF vectors. Every one of those representations threw away the order of the words. This post puts the structure back. We load the Universal Dependencies English EWT treebank, train a Conditional
In posts 23 and 24 we taught the machine to read: we loaded raw text, cleaned it with regular expressions, and ended up with a tidy table of documents. What we never did was decide what a document is once the cleaning is done. A string of characters is not
In Post 23 we closed the gap between a model that memorizes and a model that generalizes, using weight decay, dropout, and early stopping to hold the bias-variance balance. That gave us a network that fits CIFAR-10 without falling apart on the test set. But a well-regularized
In the background posts we built the pieces: tensors and automatic differentiation in Posts 2 to 5, and in Post 18 the MNIST dataset, its loader, and a first baseline model. This post puts those pieces under load. We build a multilayer perceptron on raw pixels, check a hand-derived
Post 20 gave us the probability toolkit, and Post 21 pushed uncertainty into deep models. This post keeps the same question, uncertainty, and moves it onto sequences and functions. We will read a temporal model as a graph, fit a Gaussian hidden Markov model to hourly power use, run four
In Post 3 we fit a least-squares line to a dataset and read a single number off it. In Post 4 we learned to distrust that number until we saw it on held-out rows. This post builds the machinery that replaces the single number with a distribution. We
In Post 13 we learned to ask causal questions, and in Post 14 we saw how randomization answers them cleanly. This post takes the same question into the messy world where we cannot randomize: did a job training program actually raise people's earnings? We will work through the
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 turns them into a
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
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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
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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
Stochastic Blog
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
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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
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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,
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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
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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
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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,
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In Post 2 we built the foundations with Python lists, dicts, sets, and recursion, learning how the language's core containers behave under load. Now we put those tools through their paces on a real dataset: 370,103 English words, one per line, drawn from the dwyl/english-words
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In Posts 3 and 4 we built the probabilistic foundations and learned how to update beliefs with Bayes' rule. Now we put that machinery to work on a concrete question: how much information is actually in a piece of text? This post measures that quantity on WikiText-2, a
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In Post 3 we explored the Adult Census Income dataset through distributions, correlations, and outlier fences, learning to let the data speak before we model it. Now we put that exploratory foundation to work and ask the question every data scientist eventually faces: is the pattern we see real, or
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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,