linear algebra for machine learning khan academy

The famous 3blue1brown series in linear algebra is delightful to watch and to get a solid high-level view of linear algebra. Salman Khan’s lectures are really good for absolute beginners (they are long though). Pavel Grinfeld’s lectures are my absolute favorites. I’ve consulted all these resources at one point or another. No Bullshit Guide to Linear Algebra by Ivan Savov.Introduction to Linear Algebra by Gilbert Strang.

3blue1brown’s Linear Algebra Series at YouTube.Salman Khan’s Linear Algebra Lectures at Khan Academy.Gilbert Strang’s Linear Algebra Lectures at MIT. Pavel Grinfeld’s Linear Algebra Lectures at Lemma. in Dive into Deep Learning by Zhang, Lipton, Li, And Smola. in Deep Learning by Goodfellow, Bengio, and Courville. Introduction to Applied Linear Algebra by Boyd and Vandenberghe.Mathematics for Machine Learning by Deisenroth, Faisal, and Ong.If you want to obtain a deeper understanding or to find exercises for each topic, you may want to consult those sources directly. These notes are based in a series of (mostly) freely available textbooks, video lectures, and classes I’ve read, watched and taken in the past. Further, it is a good source for people that learned linear algebra a while ago and need a refresher. It also a good introduction for people that don’t need a deep understanding of linear algebra, but still want to learn about the fundamentals to read about machine learning or to use pre-packaged machine learning solutions. If you ever get confused by matrix multiplication, don’t remember what was the $L_2$ norm, or the conditions for linear independence, this can serve as a quick reference. It is meant as a reference rather than a comprehensive review. This document contains introductory level linear algebra notes for applied machine learning. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. It is not the only ingredient, of course. Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour.

Linear algebra for machine learning khan academy