When you tell people you’re writing a machine learning book, they assume a stack: Jupyter notebooks, Markdown, maybe a static-site generator, everything on GitHub, code and prose interleaved. That’s how ML content is made now. It’s a good stack. I’m not using it.
I’m writing the whole thing in LaTeX — every chapter, every figure, every equation. In 2026, for a machine learning book, this is a slightly eccentric choice, and people ask why. Here’s the honest answer.
The math has to be beautiful, because the math is the point
The book is called Machine Learning from Scratch. The entire premise is deriving things — backpropagation by hand, gradients written out term by term, the chain rule crawling across half a page. This is not a book where equations are decoration you could screenshot from a paper. The equations are the content.
Nothing typesets math like LaTeX. Not “renders it acceptably” — typesets it, with the spacing and alignment and typographic care that makes a three-line derivation readable instead of intimidating. Markdown with a math plugin gets you 80% there and then abandons you exactly where it matters: the aligned multi-line derivation, the numbered equation you reference twelve pages later, the matrix that has to line up. For a book that lives or dies on whether a reader can follow the math, 80% is a failing grade.
A book is not a website
Notebooks and Markdown optimize for the web: scrollable, linkable, runnable. Wonderful properties — for a tutorial. But I’m writing a book, and a book is a designed physical object even when it’s a PDF. Page breaks that don’t strand a heading. Figures that sit where the eye expects them. A table of contents, an index, cross-references that know their own page numbers. Consistent typography across three hundred pages.
LaTeX was built for exactly this — it’s a typesetting system that happens to accept text input, not a text format that happens to produce output. Every serious textbook you learned from was probably set in TeX. There’s a reason the convention held.
Plain text ages well
My manuscript is a folder of .tex files. I can grep it, diff it, version it in Git, back it up anywhere, and open it in any editor on any machine for the next forty years. No proprietary format, no cloud account that might sunset, no notebook JSON that turns into a merge-conflict nightmare the moment two edits touch the same cell. When you’re committing to a multi-year project, “will I be able to open this in 2040” is not a paranoid question. LaTeX’s answer is yes, trivially.
The honest catch
I won’t pretend it’s free. LaTeX has a real learning curve, the error messages read like threats, and the edit-compile-look loop is slower than a live-rendering Markdown preview. For a quick tutorial or a runnable notebook, the modern stack genuinely wins — I’d use it without hesitation.
But for a math-dense book meant to last, the tradeoffs run the other way, and the friction is worth paying down rather than avoiding. Which — full disclosure — is the thread that connects the two things I spend my time on: the book pushed me to sharpen the editor I write it in, and the editor is the reason the friction is bearable. The manuscript and the tool grew up together. Neither would exist in its current form without the other.
So: LaTeX, in 2026, for a machine learning book. Eccentric, maybe. But every time a derivation lands clean on the page — spacing right, alignment perfect, reference resolved — I remember exactly why.
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