Subvertec’s take on this HN post? It’s a straightforward Python notebook that cuts through the fluff of linear algebra, using NumPy to walk you through everything from basic vectors and scalars to matrix ops, eigenvalues, and even SVD, all with hands-on code that lets you tinker without getting bogged down in theory. SMBs and MSPs, think of it as your quick-start toolkit for crunching data—whether you’re optimizing inventory with PCA or debugging machine learning models, it demystifies the math with practical examples that fit into your busy day. Skip the academic jargon; this guide throws you right into the code, complete with visualizations and experiments that make concepts like projections and orthogonal bases feel intuitive, not intimidating. For tech-curious makers and small-business owners, it’s a no-nonsense resource that could save hours on real projects, like building custom analytics tools or refining recommendation engines, all while keeping that irreverent vibe that says linear algebra’s actually fun.
Source: https://little-book-of.github.io/linear-algebra/books/en-US/lab.html
