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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| Original publisher | Stochastic Blog |
| Canonical URL | https://stochastic.blog/the-linear-algebra-and-calculus-behind-every-model/ |
| Publication time | Fri, 24 Jul 2026 13:45:38 +0000 |
| Retrieval time | 2026-07-24T13:52:39.305Z |
| Last seen | 2026-07-24T13:52:39.305Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | V-sX2RhjfXqC |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at Stochastic Blog.
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