[dead]
Important machine learning equations
11–20 of 39 posts
Re: Important machine learning equations
#12[deleted]
Re: Important machine learning equations
#13> 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…
Apart from the “—“, what else gives it away? Just asking from a non-native perspective.
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!
Re: Important machine learning equations
#14> 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…
Apart from the “—“, what else gives it away? Just asking from a non-native perspective.
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.
Re: Important machine learning equations
#15> 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…
Apart from the “—“, what else gives it away? Just asking from a non-native perspective.
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.
Re: Important machine learning equations
#16Earlier quoted context omitted.
Apart from the “—“, what else gives it away? Just asking from a non-native perspective.
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…
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
Re: Important machine learning equations
#17Earlier quoted context omitted.
I would echo some caution if using as a reference, as in another blog the writer states: "Backpropagation, often referred to as “backward propagation of errors,” is the cornerstone of training deep neural networks. It is a supervised learning algorithm that optimizes the weights and biases of a neural network to minimize the error between predicted and actual outputs.." https://chizkidd.github.io/2025/05/30/backpropa…
I actually see this a lot: confusing backpropagation with gradient descent (or any optimizer). Backprop is just a way to compute the gradients of the weights with respect to the cost function, not an algorithm to minimize the cost function wrt. the weights. I guess giving the (mathematically) simple principle of computing a gradient with the chain rule the fancy name "backpropagation" comes from the early days of AI…
What does this comment have to do with the previous comment, which talked about supervised learning?
Re: Important machine learning equations
#18Earlier quoted context omitted.
I would echo some caution if using as a reference, as in another blog the writer states: "Backpropagation, often referred to as “backward propagation of errors,” is the cornerstone of training deep neural networks. It is a supervised learning algorithm that optimizes the weights and biases of a neural network to minimize the error between predicted and actual outputs.." https://chizkidd.github.io/2025/05/30/backpropa…
I actually see this a lot: confusing backpropagation with gradient descent (or any optimizer). Backprop is just a way to compute the gradients of the weights with respect to the cost function, not an algorithm to minimize the cost function wrt. the weights. I guess giving the (mathematically) simple principle of computing a gradient with the chain rule the fancy name "backpropagation" comes from the early days of AI…
The German Wikipedia article makes the same mistake and it is quite infuriating.
Re: Important machine learning equations
#19I have some minor complaints but overall I think this is great! My background is in physics, and I remember finally understanding every equation on the formula sheet given to us for exams... that really felt like I finally understood a lot of physics. There's great value in being comprehensive so that a learner can choose themselves to dive deeper, and for those with more experience to check their own knowledge.
Having said that, let me raise some objections:
1. Omitting the multi-layer perceptron is a major oversight. We have backpropagation here, but not forward propagation, so to speak.
2. Omitting kernel machines is a moderate oversight. I know they're not "hot" anymore but they are very mathematically important to the field.
3. The equation for forward diffusion is really boring... it's not that important that you can take structured data and add noise incrementally until it's all noise. What's important is that in some sense you can (conditionally) reverse it. In other words, you should put the reverse diffusion equation which of course is considerably more sophisticated.
Re: Important machine learning equations
#20Earlier quoted context omitted.
I actually see this a lot: confusing backpropagation with gradient descent (or any optimizer). Backprop is just a way to compute the gradients of the weights with respect to the cost function, not an algorithm to minimize the cost function wrt. the weights. I guess giving the (mathematically) simple principle of computing a gradient with the chain rule the fancy name "backpropagation" comes from the early days of AI…
What does this comment have to do with the previous comment, which talked about supervised learning?
The previous comment highlights an example where backprop is confused with "a supervised learning algorithm".
My comment was about "confusing backpropagation with gradient descent (or any optimizer)."
For me the connection is pretty clear? The core issue is confusing backprop with minimization. The cited article mentioning supervised learning specifically doesn't take away from that.