In this video, W&B's Deep Learning Educator Charles Frye covers the core ideas from calculus that you need in order to do machine learning.
In particular, we'll see a different way of thinking about calculus -- based on linear approximations -- that makes thinking about vector- and matrix-valued derivatives easier. Then, we'll talk about the gradient descent algorithm, which is ubiquitous in machine learning, and how it arises naturally from thinking this way about calculus, and briefly touch on how calculus gets automated away.
Slides here: http://wandb.me/m4ml-calculus
Exercise notebooks here: https://github.com/wandb/edu/tree/main/math-for-ml
Check out the other Math4ML videos here: http://wandb.me/m4ml-videos
0:00 Introduction and overview
2:01 Vector calculus involves approximation with linear maps
3:48 The Fréchet derivative definition for single-variable calculus
12:50 Little-o notation makes calculus easier
16:50 The Fréchet derivative makes vector calculus easier
25:43 Gradient descent: tiny changes using calculus
34:38 Automating calculus
40:09 Additional resources