Master Calculus for Machine Learning 🚀 | Essential Concepts with Charles Frye

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