In this video, W&B's Deep Learning Educator Charles Frye covers the core ideas from linear algebra that you need in order to do machine learning.

In particular, we'll see how linear algebra is not like algebra -- it's more like programming! And then we'll build on that intuition to understand why linear algebra is so central to machine learning.

Slides here: http://wandb.me/m4ml-linear-algebra
Exercise notebooks here: https://github.com/wandb/edu/tree/mai...

Check out the other Math4ML videos here: http://wandb.me/m4ml-videos

0:00 Introduction
1:29 Why care about linear algebra?
5:15 Linear algebra is not like algebra
7:53 Linear algebra is more like programming
14:31 Arrays are an optimizable representation of functions
18:01 Arrays represent linear functions
22:34 "Refactoring" shows up in linear algebra
25:19 Any function can be refactored
28:16 The SVD is the generic refactor applied to a matrix
33:51 Using the SVD in ML
38:15 Review of takeaways and more resources