I'm guessing the answer then is "no".

That's a ridiculous metaphor as well because building a compiler is a massive software engineering project that covers a huge range of essential skills. That metaphor would work for building a computer, but not a compiler.

Clearly it shouldn't be a requirement, but it is an excellent use of a programmer's time. I can think of no software project over my career that has improved my skills more than writing a compiler.

Indeed, there are even multiple attempts to use both self-attention and convolutions in novel architectures, and there is evidence this works very well and may have significant advantages over pure vision transformer models [1-2].

IMO there is little reason to think transformers are (even today) the best architecture for any deep learning application. Perhaps if a mega-corp poured all their resources into some convolutional transformer architecture, you'd get something better than just the current vision transformer (ViT) models, but, since so much optimizations and work on the training of ViTs has been done, and since we clearly still haven't maxed out their capacity, it makes sense to stick with them at scale.

That being said, ViTs are still currently clearly the best if you want something trained on a near-entire-internet of image or video data.

[1] https://arxiv.org/abs/2103.15808

[2] https://scholar.google.ca/scholar?hl=en&as_sdt=0%2C5&q=convo...

Just do some basic searches on e.g. Google Scholar for your task (e.g. "medical image segmentation", "point cloud segmentation", "graph neural networks", "timeseries classification", "forecasting") or task modification (e.g. "'rotation invariant' architecture") or whatever, sort by year, make sure to click on papers that have a large number of citations, and start reading. You will start to get a feel for domains or specific areas where transformers are and are not clearly the best models. Or just ask e.g. ChatGPT Thinking with search enabled about these kinds of things (and then verify the answer by going to the actual papers).

Also check HuggingFace and other model hubs and filter by task to see if any of these models are available in an easy-to-use format. But most research models will only be available on GitHub somewhere, and in general you are just deciding between a vision transformer and the latest convolutional model (usually a ConvNext vX for some X).

In practice, if you need to work with the kind of data that is found online, and don't have a highly specialized type of data or problem, then you do, today, almost always just want some pre-trained transformer.

But if you actually have to (pre)train a model from scratch on specialized data, in many cases you will not have enough data or resources to get the most out of a transformer, and often some kind of older / simpler convolutional model is going to give better performance at less cost. Sometimes in these cases you don't even want a deep-learner at all, and just classic ML or algorithms are far superior. A good example would be timeseries forecasting, where embarrassingly simple linear models blow overly-complicated and hugely expensive transformer models right out of the water (https://arxiv.org/abs/2205.13504).

Oh, right, and unless TabPFNv2 (https://www.nature.com/articles/s41586-024-08328-6) makes sense for your use-case, you are still better off using boosted decision trees (e.g. XGBoost, LightGBM, or CatBoost) for tabular data.

>the terms "Query" and "Value" are largely arbitrary and meaningless in practice

This is the most confusing thing about it imo. Those words all mean something but they're just more matrix multiplications. Nothing was being searched for.

Do you think the dimension reduction is necessary? Or is it just practical (due to current hardware scarcity)?

If you have 20 hours to spare I highly recommend this youtube playlist from Andrej Karpathy https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxb...

It starts with the fundamentals of how backpropagation works then advances to building a few simple models and ends with building a GPT-2 clone. It won't taech you everything about AI models but it gives you a solid foundation for branching out.

The most valuable tutorial will be translating from the paper itself. The more hand holding you have in the process, the less you'll be learning conceptually. The pure manipulation of matrices is rather boring and uninformative without some context.

I also think the implementation is more helpful for understanding the engineering work to run these models that getting a deeper mathematical understanding of what the model is doing.

In some respects, yes. There is no single human being with a general knowledge as vast as that of a SOTA LLM, or able to speak as many languages. Claude knows about transformers more than enough to explain them to a layperson, elucidating specific points and resolving doubts. As someone who learns more easily by prodding other people's knowledge rather than from static explanations, I find LLMs extremely useful.