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Ryan Orban

Ryan Orban

Subject
7 entries

Linear Algebra

Bookmarks

  1. Linear Representations and Superposition in LLMs

    A clear explainer on how LLMs encode thousands of features in relatively small embedding spaces via superposition — the Johnson-Lindenstrauss lemma applied to neural representations. Good primer on the theoretical foundations behind SAE-based interpretability.

  2. Linear Algebra — Math for Machine Learning

    A YouTube course on linear algebra specifically framed for machine learning — covering vectors, matrices, eigenvalues, and the operations that underlie neural networks. Useful complement to theoretical ML reading when the math intuition is missing.

  3. Spectral Graph Embedding

    Thomas Bonald's lecture notes (Institut Polytechnique de Paris, 2019-2020) introduce spectral methods for graph embedding — Laplacian eigenmaps, spectral clustering, and random walk connections — grounding graph representation learning in linear algebra. These notes are foundational for understanding why GCN works and where its limitations come from.

  4. Linear Algebra Done Wrong

    Sergei Treil's free textbook on linear algebra, written for mathematically advanced undergrads and deliberately structured around abstract reasoning rather than computational cookbook recipes. The title is a joke — it's done 'wrong' in the sense of prioritizing theory and proof over drill.

  5. An Intuitive Guide to Linear Algebra

    BetterExplained's intuitive guide to linear algebra — builds geometric intuition for vectors, matrices, and transformations rather than drilling algebraic procedures. The right starting point before the formal treatment.

  6. Sparse Matrices in SciPy

    A visual explainer of sparse matrix formats in SciPy (COO, CSR, CSC, LIL, DOK) with animated illustrations showing how data is stored. Essential reading before working with high-dimensional feature matrices in ML or graph algorithms.

  7. Introduction to Principal Component Analysis (PCA)

    Laura Diane Hamilton's accessible introduction to Principal Component Analysis — covering the geometric and algebraic intuition without requiring a linear algebra background. One of the cleaner beginner explanations of a technique that's notoriously hard to explain well.

All bookmarks