[DoingIsLearning]

There are also different styles of teaching and learning. Karpathy always like to start from first principles and increment the building blocks.

Whereas for example Jeremy Howard's style resonates a lot more with how I enjoy learning, very much a "let's build it" and then tinker around to gain intuition on how things inside the box are working.

I see the benefit in both approaches and perhaps Karpathy is more methodical and robust. But I just find Howard's top-down style a lot easier to stay motivated with when I am learning on my own time.

[janalsncm]

Definitely agree. I think a lot of people get hung up on the math in ML but honestly there are so many other things you could spend time on, and there are opportunity costs for everything.

So I say, build the thing, figure out where the shortcomings in your knowledge are, and continue refining. One of those things will inevitably be math. Maybe it will be signals processing the next week or fundamentals of Spark the next. And there are always interesting papers coming out.

[vimgrinder]

I definitely avoided ML for years just due to math. But having a chatbot who can explain math with examples in any style you want defintely changed my opinion about math and ML in general. A big barrier to math is how it's written imo and not explained in a fun way with lot of examples. I certainly don't have a mathy brain, but I do get things when explained with examples (and certainly find it hard to come up with my own examples while fighting with the symbols).

[attentionmech]

Will checkout jeremy's lectures. I actually use his fastbook notebooks a lot to self-study.

Karpathy's style, for me is more like at the right abstraction to bring out curiosity in me towards the subject. After watching his lectures, i go on to more materials generally, and never really stop there.