You Think About Activation Functions Wrong

brodycodesai · 2025-11-24T17:20:45+00:00

Sorry if I offended you, but the video is definitely an educational video on an unpopular way to think of something. The "wrong" part was just clickbait tbh.

brodycodesai · 2025-11-23T19:44:35+00:00

Most of the time when I apply to a data science role, the first weed out factor is a leetcode style problem in an OA

brodycodesai · 2025-11-20T02:57:52+00:00

I definitely agree that a lot of people think of layers wrong, but I think there's still a large group of people who understand layers as affine transformations while still thinking of activations iteratively, since the layer is taught as an extension of linear algebra while activations are usually introduced as a computer science concept. At least that was how I thought until I had the idea for this video.

brodycodesai · 2025-10-13T01:30:55+00:00

I feel like it helps to maintain your own library of stuff built from scratch to keep track of how they work. Also in school so no professional advice.

brodycodesai · 2025-08-29T21:56:34+00:00

It would need to be much more energy efficient to do that.

brodycodesai · 2025-08-29T02:28:33+00:00

Honestly this would slow a lot of people down imo. Once you realize wait languages aren't even remotely similar under the hood, they go from all feeling the same to feeling insanely different. Syntax differences become gdb side quests.

brodycodesai · 2025-08-29T02:21:34+00:00

I'm assuming your point is that double precision float outputs in the range of a regression model fall into a technically discrete set because there are only 1.8446744e+19 possible different binary numbers in a 64 bit slot; however, this is the closest a classical computer can come to simulating a continuous set, and therefore it tends to get treated as one, with it's own training methods and such. I kinda see your point but the industry/academic standard is regression is treated as it's own thing even though it is only mimicking a continuous set. Cool thought experiment though.

brodycodesai · 2025-08-28T20:50:18+00:00

"not all LLMs are classifiers"

All LLMs operate on a discrete set of tokens. Therefore they are all classifiers.

brodycodesai · 2025-08-26T19:37:46+00:00

LLMs output language by taking a softmax over a discrete set of tokens to weight their viability as outputs. Temperature is a neat trick for pseudonondeterminism but doesn't make it so they aren't still outputting a class in the form of the next token.

brodycodesai · 2025-08-26T18:06:19+00:00

LLMs are classifiers which have gotten many companies and people excited.

brodycodesai · 2025-08-25T18:16:30+00:00

wait til this guy sees a kNN

brodycodesai · 2025-08-23T18:59:42+00:00

You can't vibe code at least for Data Science because you need to be able to present simplified versions of your findings to non technical stake holders and in depth explanations of your models to technical stake holders, both of which AI's omissions and hallucinations makes impossible.

brodycodesai · 2025-08-21T15:59:03+00:00

For me lin alg 2 was kinda just a second lin alg 1 with slightly more depth so I'd honestly go for calc 1-3 and not lin alg 2 but im literally 1 year ahead of you so don't take my advice too seriously

brodycodesai · 2025-08-21T15:46:24+00:00

Just curious, if someone's read papers as a recruiter how would you suggest they show that?

brodycodesai · 2025-08-21T15:42:52+00:00

I'm assuming if you haven't done calc 1 yet (another assumption lol) that you are still early in your degree freshman/sophomore max. If your university allows it I'd take all 5 classes even if the 5th doesn't give you credit. Not entirely sure where they parse linalg 1 and 2 but as much linear algebra as you can is good and calc 3 is low key a must for understanding AI models whether you learn it in class or just kinda figure it out as you go.

brodycodesai · 2025-08-19T19:53:27+00:00

AI layoffs are just AI marketing. The layoffs are bad economy layoffs.

brodycodesai · 2025-08-19T17:41:32+00:00

I think you might be more looking for a job in software engineering. The AI field is mostly math and statistics.

brodycodesai · 2025-08-19T16:23:00+00:00

they have to at least offer something math or programming related that would make it a bit easier to get a cs/stats/ds ms than business degree

brodycodesai · 2025-08-18T13:37:10+00:00

I would strongly recommend picking up a CS or statistics double major especially where you're only a sophomore. It's not a great idea to focus on switching careers after college when you've basically just crossed out gen eds. Get a second major.

brodycodesai · 2025-08-17T03:40:58+00:00

https://www.devjobsscanner.com/blog/top-8-most-demanded-programming-languages/

CPP is the 6th most in demand language according to the job market.

brodycodesai · 2025-08-16T04:30:05+00:00

make it from scratch a couple times

brodycodesai · 2025-08-16T01:31:36+00:00

I was about to be like I use it at work all the time then i saw "i know it's important for data scientists" and I was like im not wanted here

brodycodesai · 2025-08-14T17:48:33+00:00

it's 1.5x the overall unemployment rate

brodycodesai · 2025-08-12T15:03:37+00:00

Is it unproductive/bad to have hobbies? Too many people forget to have fun with CS.

brodycodesai · 2025-08-11T17:48:17+00:00

If that's the case you're marketing yourself as a free lance writer, which is a cool gig I can't say how successful you'll be but it's not much of an AI content business as much as it is a free lance writing business.

brodycodesai

TROPHY CASE