I still don’t understand the process of learning ML, like sure we build micrograd but is it only didactic exercise or can we use it to train it to do something serious on our own hardware?
I don’t understand this comment, for one we’re engineers/hackers and should be curious how this stuff works. It’s exciting. Practically speaking this is like asking why learn how to write a simple forum or blog when we can’t host Facebook on our on hardware: it’s going to be hard to work on the latest models if you don’t first understand the basics.
You can totally do some visual classification problems (like object detection) on current consumer hardware. Even more. You can also take some smaller existing language models and fine tune them for some special task - also completely feasible.
I guess it depends on what you mean by serious. Pre-training a competitive LLM with current methods and consumer hardware is prohibitive for sure. Solving a classification problem could be totally doable depending on the domain.
You'll probably need some hardware acceleration. There's a good course that builds something like micrograd in the beginning and extends on it: https://dlsyscourse.org/lectures/
8 GB of memory is a little restrictive but still usable for many problems like visual classification problems. Training would take a little more time though as you can't do as much batch processing as it would be possible with more memory.
It should be worth to do these kind of hardware accelerations with any kind of hardware that has more TFlop/s (and memory bandwidth) than your CPU basically.
