I'm just trying to develop the lens where I can see a problem and know what properties of it are meaningful from an ML standpoint.

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.

Or just train some NNs. If more time write the code and understand the tensor operations.

I've bounced off of many good textbooks. Even Karpathy's YouTube series was too dense for me. I'm trying to come in at a more palatable level.

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.

It's not a trick bud. The github page shows my user name and Claude. The content is intended to be read by an AI agent and explored through a text interface. That is explicit in the readme and the primer itself.

If you think you can generate this artifact with a prompt then show me. This was 2 days of exploration and research.

I got to that and just stopped reading!

Block pages that contain a construct that is commonly used by humans in human writing and has been for centuries because AI that trained on human writing also uses it?

Should we also block pages that contain no spelling errors or no grammatical errors?

Can we also ban anything where the second line is "let that sink in"? And anything claiming that "X is a masterclass in Y" (especially for (tweet, empathy))?

Careful, you'll just make the output generators harder to spot! Www We need to keep their 'Tells' hidden from them..

my apologies it wasn't up to your standards. In fairness to me, that line is exactly what my effort was. I wasn't trying to "learn ML" I am trying to build a mental model that let's me decompose real problem into ML primitives.

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.

Framing a business problem in terms of ML is indeed important. Where does classification come in, where does regression come in, when to use retrieval, when to use generative solutions. Would be a good section to add imo.

I quite liked this. It feels approachable and to-the-point.

thanks Oleg