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Comment by zingar | Hacker News Reader

I have lots of non-AI software experience but nothing with AI (apart from using LLMs like everyone else). Also I did an introductory university course in AI 20 years ago that I’ve completely forgotten.

Where do I get to if I go through this material?

Enough to build… what? Or contribute on… ? Enough knowledge to have useful conversations on …? Enough knowledge to understand where to … is useful and why?

Where are the limits, what is it that the AI researchers have that this wouldn’t give?

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Strange question. If you don’t know why you need this, you probably don’t. It will be the same as with the introductory AI course you did 20 years ago.

Well, no ... For a start any "AI" course 20 years ago probably wouldn't have even mentioned neural nets, and certainly not as a mainstream technique.

A 20yr old "AI" curriculum would have looked more like the 3rd edition of Russel & Norvig's "Artificial Intelligence - A Modern Approach".

https://github.com/yanshengjia/ml-road/blob/master/resources...

Karpathy's videos aren't an AI (except in modern sense of AI=LLMs) course, or a machine learning course, or even a neural network course for that matter (despite the title) - it's really just "From Zero to LLMs".

I think they meant the result— not the content—would be the same.

Neural nets were taught in my Uni in the late 90s. They were presented as the AI technique, which was however computationally infeasible at the time. Moreover, it was clearly stated that all supporting ideas were developed and researched 20 years prior, and the field was basically stagnated due to hardware not being there.

I remember reading "neural network" articles back from late 80's, early 90's, which weren't just about ANNs, but also other connectionist approaches like Kohonen's Self-Organizing Maps and Stephen Grossberg's Adaptive Resonance Theory (ART) ... I don't know how your university taught it, but back then this seemed more futuristic brain-related stuff, not a practical "AI" technique.

Anyone who watches the videos and follows along will indeed come up to speed on the basics of neural nets, at least with respect to MLPs. It's an excellent introduction.

Sure, the basics of neural nets, but it seems just as a foundation leading to LLMs. He doesn't cover the zoo of ANN architectures such as ResNets, RNNs, LSTMs, GANs, diffusion models, etc, and barely touches on regularization, optimization, etc, other than mentioning BatchNorm and promising ADAM in a later video.

It's a useful series of videos no doubt, but his goal is to strip things down to basics and show how an ANN like a Transformer can be built from the ground up without using all the tools/libraries that would actually be used in practice.

My introductory course used that exact textbook and I still have it on my shelf :).

It has a chapter or two on NNs and even mentions back propagation in the index, but the majority of the book focuses elsewhere.

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