The latter isn’t wrong or useless. It’s simply not something a typical software engineer will need.
On the other hand, wiring up LLMs into an application is very popular and may be an engineer’s first experience with systems that are fundamentally chaotic. Knowing the difference between precision and recall and when you care about them will get you a lot more bang for your buck.
I would suggest the gateway drug into ML for most engineers is something like: we have a task and it can currently be done for X dollars. But maybe we can do it for a tenth of the price with a different API call. Or maybe there’s something on Huggingface that does the same thing for a fixed hourly cost, hundreds of times cheaper in practice.
Coming from a specific domain where I have a sharpened instinct for how things are haven't really given me the ability to decompose the problem using ML primitives. That's what I'm working on.
This was a two day exploration where I provided the syllabus and ran through it with Claude Code, asking questions, trying to anchor it to stuff I understand well. I feel like the artifact has value.
"This isn't a textbook or a tutorial. It's a mental model — the abstractions you need to reason about ML systems the way you already reason about software systems."
If you think you can generate this artifact with a prompt then show me. This was 2 days of exploration and research.
I appreciate the former and am trying to filter the latter.
Should we also block pages that contain no spelling errors or no grammatical errors?
It's unclear to me if you think the resource has no value or if it bother you that I wrote it using a coding agent.
I wrote the syllabus and worked through each section. Where my understanding was weak I explored the space, pulled in research, referred the model to other sources, and just generally tried to ground the topic in something I understood.
What resulted was something that helped a lot of subjects click together for me. Especially when to reach for a particular activation function and the section on gating.
This enter survey was motivated by an ML expirent I ran with assosicative memories that just failed horribly. So rather than post mortem that I set about understanding why it failed.
Anyhow, thank you for the feedback. I submitted this in good faith that it may help others.
I know how to do it and it's all internalized. Even if I've never needed to do it.
that's the toolbox I'm trying to develop in ML. For example. I've studies LSTM and implemented one. What I didn't know was if gating was turing complete and essentially unbound. I didn't know if gates could be arbitrarily complex. Importantly I had no idea how to translate "I need a switch here" to a gate, or if a switch was even possible given the need to be differentiable for backprop.
read it from top to bottom or better have your favorite language model read it and then explore the space with a strong guided syllabus.
