[mywaifuismeta]

I'm generally not a fan of these kind of high-level tutorials that tell you "use X library to get Y result" - it's just not good for learning. But any content that tries to sell you on learning ML/NLP/etc in a few weeks is just that. I understand people want to make money by targeting a large audience, but it makes me sad when I see (the vast vast majority) of practitioners not having any understanding about ML (or NLP) and just blindly applying libraries.

I don't think you necessarily need a linguistics background for NLP, but I think you need either a strong linguistics OR ML background so that you know what's going on under the hood and can make connections. Anyone can call into Huggingface, you don't need a course for that.

[scarface74]

Everything eventually gets boiled down to libraries. The purpose of technology is to get things done. I could say the same that it makes me sad that today’s developers use high level languages without ever knowing assembly. A chip designer could say that assembly language developers are saddened that the assembly language programmer never had to learn how processors are created.

[mirker]

It’s fine when the library is a tight abstraction. Unfortunately, ML libraries are leaky.

Example: take a classification model and change the output dimensions without understanding the model.

[nmstoker]

Yes, the challenge people then face is that if they lack too much intuition for the subject, they can't spot obvious issues.

We've all seen how ML people don't necessarily have to have the skills to solve a problem (ie i don't need to speak Vietnamese to make a "passable" ML translator) but it's not long before the lack of knowledge starts to show up embarrassing shortfalls - being too arms length about any topic is a recipe for disaster!

[Der_Einzige]

Doing non trivial things (more than .train or .generate) with huggingface models def requires tutorials or other resources, not sure what you're on about at all.