[sabertoothed]

I think you completely misunderstand his stance.

You don't need the math in the beginning to train a model and get first results. Later, you will need the math and Jeremy clearly knows the math.

He gives a great example: In sports, you don't start with learning about physiology and train individual muscles etc. (I paraphrase), you start playing basketball or baseball or soccer, and understand the overall game. And if you like it, you can then become better and better and get deeper and deeper.

It's not helpful to start with linear algebra if - what motivated you - was the application of ML. We lose people who could have otherwise become experts later.

[sabertoothed]

It is far from anti-intellectualism. It is about didactics. And Jeremy is spot on about this.

[rg111]

It is good enough to not need heavy math to begin.

Yeah, I know.

But you need a lot of math to do Deep Learning.

But I do not think Howard tries to communicate that.

You can't show me people who knows high school math only and gets to work in FAANG, or PhD in DL/related, or CTO of an AI start-up, or anyhow "made it" in DL.

[idf00]

I think co-writing and co-teaching a math course at a deep learning company he co-founded, pinning that to the github repo and moving it close to the top of the home page makes it pretty clear he does see value in math in deep learning. I mean, if you need other evidence beyond than the fact that he teaches math needed to understand and build things from scratch in the deep learning course...

In the courses he has always been clear you don't need a ton of math to begin. He's also always been clear that as you progress you will encounter math that you need to learn to continue. He's always clear that that is ok if you don't know it before you start and it's ok to learn it when you need it.