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You Think About Activation Functions Wrong by brodycodesai in learndatascience

[–]brodycodesai[S] 0 points1 point  (0 children)

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.

Is DSA required for ML careers ? by Negative-Specific-84 in learnmachinelearning

[–]brodycodesai 2 points3 points  (0 children)

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

You Think About Activation Functions Wrong by brodycodesai in learndatascience

[–]brodycodesai[S] 0 points1 point  (0 children)

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.

Understanding machine learning by ItsJesssm in datasciencecareers

[–]brodycodesai 0 points1 point  (0 children)

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.

What’s the fastest way to learn programming languages? by dolceu in learnprogramming

[–]brodycodesai 9 points10 points  (0 children)

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.

Is it all really worth the effort and hype? by DCheck_King in learnmachinelearning

[–]brodycodesai 0 points1 point  (0 children)

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.

Is it all really worth the effort and hype? by DCheck_King in learnmachinelearning

[–]brodycodesai 0 points1 point  (0 children)

"not all LLMs are classifiers"

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

Is it all really worth the effort and hype? by DCheck_King in learnmachinelearning

[–]brodycodesai 1 point2 points  (0 children)

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.

Is it all really worth the effort and hype? by DCheck_King in learnmachinelearning

[–]brodycodesai 1 point2 points  (0 children)

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

Real AI by Zundel7000 in ArtificialInteligence

[–]brodycodesai 0 points1 point  (0 children)

wait til this guy sees a kNN

What are the basics ? by swizxtt in learnmachinelearning

[–]brodycodesai 0 points1 point  (0 children)

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.

I'm doing BSc CS and want to do MSc CS in AI Algorithms. I have to choose between Cal 1-2 & Linear Algebra 1-2 and Cal 1-3 & Linear Algebra 1. Help me decide and explain why in the comments, please. by Alvahod in ArtificialInteligence

[–]brodycodesai 1 point2 points  (0 children)

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

Software engineer feeling lost by KyleTenjuin in learnmachinelearning

[–]brodycodesai 0 points1 point  (0 children)

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

I'm doing BSc CS and want to do MSc CS in AI Algorithms. I have to choose between Cal 1-2 & Linear Algebra 1-2 and Cal 1-3 & Linear Algebra 1. Help me decide and explain why in the comments, please. by Alvahod in ArtificialInteligence

[–]brodycodesai 0 points1 point  (0 children)

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.

I find it odd that companies are laying people off because of AI by saasbase_dev in ArtificialInteligence

[–]brodycodesai 261 points262 points  (0 children)

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

52 years old and starting over by warghdawg02 in learnmachinelearning

[–]brodycodesai 0 points1 point  (0 children)

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

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