I’ve gone through this series of videos earlier this year.

In the past I’ve gone through many “educational resources” about deep neural networks - books, coursera courses (yeah, that one), a university class, the fastai course - but I don’t work with them at all in my day to day.

This series of videos was by far the best, most “intuition building”, highest signal-to-noise ratio, and least “annoying” content to get through. Could of course be that his way of teaching just clicks with me, but in general - very strong recommend. It’s the primary resource I now recommend when someone wants to get into lower level details of DNNs.

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3abiton 4 days ago | parent [-]

Karpathy has a great intuitive style, but sometimes it's too dumbed down. If you come from adjacent fields, it might be a bit dragging, but it's always entertaining

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ronbenton 4 days ago | parent [-]

>Karpathy has a great intuitive style, but sometimes it's too dumbed down

As someone who has tried some teaching in the past, it's basically impossible to teach to an audience with a wide array of experience and knowledge. I think you need to define your intended audience as narrowly as possible, teach them, and just accept that more knowledgeable folk may be bored and less knowledgeable folk may be lost.

	▲	mlmonkey 3 days ago \| parent \| next [-]
		When I was an instructor for courses like "Intro to Programming", this was definitely the case. The students ranged from "have never programmed before" to "I've been writing games in my spare time", but because it was a prerequisite for other courses, they all had to do it. Teaching the class was a pain in the ass! What seemed to work was to do the intro stuff, and periodically throw a bone to the smartasses. Once I had them on my side, it became smooth sailing.
	▲	miki123211 3 days ago \| parent \| prev [-]
		I think this is where LLM-assisted education is going to shine. An LLM is the perfect tool to fill the little gaps that you need to fill to understand that one explanation that's almost at your level, but not quite.