>Spending your precious life learning them is a waste

So you really did not learn them.

There is nothing wrong with being user. You don't have to know how compilers work to use compiler. But then you should not say you understand compilers.

In the same way, you probably would benefit from a book "Using deep learning", not "Understanding deep learning".

I know them and am a founder of a vc funded ai startup. Nobody is deploying naive bayes algorithms

> Nobody is deploying naive bayes algorithms

Exactly my point. You are so into user perspective that you think you are arguing against me.

Yes, they’re not deploying them. That doesn’t mean it doesn’t still help to know the fundamentals of the field, especially when you’re trying to innovate.

Yeah. But you didn’t build a plane without knowing physics right?

Nobody deploys a textbook algorithm because everyone knows textbooks algorithms and there are no advantages. So, no, there is real value in learning the fundamentals, dear founder.

I started my journey in machine learning fifteen years ago. Ironically, at that time, my professor told me that neural networks were outdated and trying them wouldn't result in publishable research. SVMs were popular and emphasized in my coursework. I concur that SVMs don't hold as much practical significance today. But the progress in AI and ML is generally unpredictable, and no one knows what theory leads to the next leap in the field.

So what? The same fundamental machine learning concepts are still relevant to deep learning.

It's almost like arguing that everything you learned as a Java developer is completely useless when a new programming language replaces it.

This brings up an important question: Is a topic useful to learn if you will never use it in your life?

To attempt answering this question, we can look at LLMs as an analogy. If you include code in the training set for an LLM, it also makes the LLM better at non-coding tasks, suggesting that sometimes learning something makes you also better at other things. I'm not saying the same necessarily applies for learning these "old school" AI techniques, but it's a decently analogy at least.