I agree that the tone of my original comment was overly snarky.

The challenge to learn ML for a middle-aged dentist is immense, and probably not where his best uses lie. I was fearful most on here would just tell him to take the plunge. But I see that others have gotten my point across much more eloquently (and not as snarky ;) ).

Only a small minority is able to make significant technical contributions. I may have misinterpreted his angle of groundwork, but as he mentioned resources to try and learn ML I assumed he meant technical. As I said domain knowledge and/or providing data and relying on the technical expertise of others is the most valuable direction to go. I think we are actually in agreement here.

I myself work with engineering groups on some applied projects and a lot of the 'applied ML' outside of CS is absolutely horrendous.

I admire your passion and good intentions, but why do people think they can fundamentally contribute (laying the groundwork) to the SOTA of a technical field without any background and limited time. Thousands of PhDs working on AI dedicate almost every minute of their lives to this field, and only a small minority is able to make significant contributions that lay the groundwork for anything at all.

Nobody expects to be able to become a surgeon as a hobby; why do people expect to do excellent ML research as a hobby? Additionally, with all due respect, your paragraph further shows very limited understanding of current surgical robotic technology and ChatGPT.

The best way you can contribute is to work with ML experts and offer domain knowledge or data — do not expect to contribute on a technical/foundational level as a hobbyist.

How are Siamese networks used for spend classification?

Could you elaborate a bit more on the methodology?