Important machine learning equations

> This blog post has explored the most critical equations in machine learning, from foundational probability and linear algebra to advanced concepts like diffusion and attention. With theoretical explanations, practical implementations, and visualizations, you now have a comprehensive resource to understand and apply ML math. Point anyone asking about core ML math here—they’ll learn 95% of what they need in one place!

It makes me sad to see LLM slop on the front page.

Apart from the “—“, what else gives it away? Just asking from a non-native perspective.

Not op, but it is very clearly the final summary telling the user that the post they asked the AI to write is now created.

This is probably not going to be a very helpful answer, but I sort of think of it this way: you probably have favorite authors or artist (or maybe some really dislike!), where you could probably take a look at a piece of their work, even if its new to you, and immediately recognize their voice & style.

A lot of LLM chat models have a very particular voice and style they use by default, especially in these longer form "Sure, I can help you write a blog article about X!" type responses. Some pieces of writing just scream "ChatGPT wrote this", even if they don't include em-dashes, hah!

It's not really about the language. If someone doesn't speak English well and wants to use a model to translate it, that's cool. What I'm picking up on is the dishonesty and vapidness. The article _doesn't_ explore linear algebra, it _doesn't_ have visualizations, it's _not_ a comprehensive resource, and reading this won't teach you anything beyond keywords and formulas.

What makes me angry about LLM slop is imagining how this looks to a student learning this stuff. Putting a post like this on your personal blog is implicitly saying: as long as you know some some "equations" and remember the keywords, a language model can do the rest of the thinking for you! It's encouraging people to forgo learning.

It's just too bombastic for what it is - listing some equations with brief explanation and implementation.

If you don't know these things on some level already the post doesn't give you too much (far from 95%), it's a brief reference of some of the formulas used in machine learning/AI.

OK, on reflection, there are a few things,

Kace's response is absolutely right that the summaries tend to be a place where there is a big giveaway.

There is also something about the way they use "you" and the article itself... E.g. the "you now have a comprehensive resource to understand and apply ML math. Point anyone asking about core ML math here..." bit. This isn't something you would really expect to read in a human written article. It's a ChatBot presenting it's work to "you", the single user it's conversing with, not an author addressing their readers. Even if you ask the bot to write you an article for a blog, a lot of times it's response tends to mix in these chatty bits that address the user or directly references to the users questions / prompts in some way, which can be really jarring when transferred to a different medium w/o some editing

Slop brings back memories of literature teachers red-marking my "bombastic" terms in primary school essays

Three things come to mind:

- bold-face item headers (eg “Practical Significance:”)

- lists of complex descriptors non-technical parts of the writing (“ With theoretical explanations, practical implementations, and visualizations”)

- the cheery, optimistic note that underlines a goal plausibly derived from a prompt. (eg “ Let’s dive into the equations that power this fascinating field!”)

As someone who tended to use "—" in a lot of my writing naturally before, the prevalence of its usage by LLMs frustrate me a lot. I now have to rewrite things that felt natural just so no one will think I'm an LLM.

I stopped reading the post before that and went back to check. It's so blatant...especially when it mentions visualizations.

> With theoretical explanations, practical implementations, and visualizations, you now have a comprehensive resource to understand and apply ML math. Point anyone asking about core ML math here—they’ll learn 95% of what they need in one place!