I tried to write my own textbooks and almost lost my mind

A student, Enoch Yu, created two open-source repositories—notebook-linear-algebra and LibreNotebook—to help other students learn the math and practical skills needed for machine learning and AI. The linear algebra repository focuses on fundamental concepts for ML, while the machine learning notebook aims to bridge theory and implementation. Yu invites feedback to improve the materials.

I tried to write my own textbooks and almost lost my mind If you are currently drowning in the math required to understand how an LLM agent actually functions, or if you are stuck staring at a matrix multiplication like it's a cryptic ancient curse, these might actually help you. I’m a student, so please don't come for me with your PhDs—this is a learning project, not a peer-reviewed journal. I've split these into two main repositories because trying to cram everything into one folder was a deployment nightmare. The Linear Algebra Repository This is where the heavy lifting happens. If you don't understand the underlying math, you're basically just playing with fancy calculators without knowing why the numbers are moving. This notebook covers the essentials for anyone trying to build a real-world AI workflow. Content focus: Fundamental concepts required for ML. Format: Jupyter Notebooks perfect for that hands-on guide feel . Repo link: https://github.com/enochyu-official/notebook-linear-algebra The Machine Learning Notebook This one is a bit of a work in progress don't look too closely at the unfinished sections, I'm still figuring things out , but it's intended to be a complete guide to the practical application of the math mentioned above. It’s essentially my attempt at a practical tutorial for moving from "I know what a vector is" to "I can actually implement something." Status: Mostly done, but still evolving. Core goal: Bridging the gap between theory and implementation. Repo link: https://github.com/enochyu-official/LibreNotebook I know, I know—the internet is already flooded with "complete guides" and "beginner-friendly" tutorials. But since these were written by a student for students, the perspective is a little different. I'm not trying to sound like a professor; I'm just trying to document the struggle of making sense of it all. If you find a typo or a mathematical error that makes you want to throw your monitor out a window, please just let me know. I’m using this as a deep dive to solidify my own knowledge, so your feedback is basically a free debugging service for my brain. Next Ei-Core just said no to a retention estimate — and that refusal → /en/threads/7222/