The linear algebra and calculus behind every model
Stochastic Blog The linear algebra and calculus behind every model The Stochastic Blog Team 24 Jul 2026 — 8 min read Share 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 scratch using the MNIST dataset, and by the end we'll have compressed images with the SVD, computed gradients by hand, and solved a constrained optimization problem.
- ▪Stochastic Blog The linear algebra and calculus behind every model The Stochastic Blog Team 24 Jul 2026 — 8 min read Share 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 scratch using the MNIST dataset, and by the end we'll have compressed images with the SVD, computed gradients by hand, and solved a constrained optimization problem.
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Stochastic Blog The linear algebra and calculus behind every model The Stochastic Blog Team 24 Jul 2026 — 8 min read Share 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 scratch using the MNIST dataset, and by the end we'll have compressed images with the SVD, computed gradients by hand, and solved a constrained optimization problem.
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