The Little Book of Linear Algebra

Original Article ↗ Hacker News Discussion ↗

Format, tooling, and distribution

  • Readers praise the compact, point-form style and clean LaTeX source; some say the .tex is almost readable as text.
  • The EPUB uses HTML + MathML generated via Pandoc, which several people find notably clean and modern.
  • GitHub’s LaTeX rendering works well up to a point, then fails with “[Unable to render expression]”, leading to speculation about an internal limit and prompting some to switch to the EPUB.
  • A few users report issues: PDF not opening, and a typo in the GitHub org name (“litte”).
  • Suggestions include adding a pointer to the repo inside the book itself.

Target level and suitability as a resource

  • Many see it as a great refresher or compact reference rather than a full teaching text.
  • Several criticize “beginner-friendly” claims: set-theoretic notation appears immediately, symbols are not always introduced, and some concepts (eigenspaces, normal equations) are mentioned without proper definition or derivation.
  • Others argue there is no universally beginner-friendly level; if basic set notation is unfamiliar, a reader may need more preliminary material.
  • There’s appreciation that it’s CC-licensed and free, but some feel that lack of proofs, illustrations, and motivation limits its use as a first exposure.

Teaching linear algebra: motivation and difficulty

  • Strong debate over whether linear algebra basics are inherently boring or just badly taught.
  • One camp says you must “power through” the mechanics (vectors, matrices, Gaussian elimination) before the interesting parts (eigenvalues, SVD, applications) become visible.
  • Others insist matrix multiplication and linear transformations can be motivated early via composition of linear maps, geometry, or graphics (rotations, reflections), though examples like translation spark technical corrections (not linear in Euclidean space).
  • Multiple commenters stress geometric and visual intuition, advocating graphics programming, 2D/3D examples, and tools like Processing or GeoGebra.
  • There’s recurring criticism of how schools over-emphasize algorithmic calculus while under-teaching vectors, matrices, probability, and statistics.

Alternative resources and broader ecosystem

  • Numerous recommendations surface: more visual/interactive notes, standard and “no-bullshit” textbooks, geometry-focused intros, Axler-style “matrices later” approaches, and OCW/YouTube courses.
  • 3Blue1Brown’s linear algebra series is repeatedly highlighted as making concepts “click”.
  • Some note that AI/LLMs are now effective helpers for decoding unfamiliar notation from PDFs or screenshots.
  • The author mentions a similar “little book” on calculus and an in-progress broader series on math and CS.