I have been using this and other resources to learn linear and logistic regression .
An Introduction to Statistical Learning with Applications in Python
61–70 of 87 posts
Re: An Introduction to Statistical Learning with Applications in Python
#62Earlier quoted context omitted.
What you are saying is true. This kind of books are key to getting started with Machine Learning/AI, and this particular book is a very good one. I started my ML journey with this book. There is a lot of hype around AI and it is going to be like the dotcom bubble. Unlike Crypto, AI has real uses right now and I am saying this not taking into account any LLM products. But there is also a lot of hype and wishful thinki…
> and this bubble is going to burst and hurt a lot of people > But AI is here to stay. And even after the bubble bursts, there will be real uses of AI all around us. This is, as a ML researcher, exactly where I'm at (in belief). The utility is high, but so is the noise. Rather, the utility is sufficient. The danger of ML is not so much X-risk or malevolent AGI, but dumb ML being used inappropriately. And in general,…
> This is, as a ML researcher, exactly where I'm at (in belief).
As an ML researcher myself, I'm glad to see other researchers maintaining a level-headed approach amidst the noise.
Re: An Introduction to Statistical Learning with Applications in Python
#63Earlier quoted context omitted.
> and this bubble is going to burst and hurt a lot of people > But AI is here to stay. And even after the bubble bursts, there will be real uses of AI all around us. This is, as a ML researcher, exactly where I'm at (in belief). The utility is high, but so is the noise. Rather, the utility is sufficient. The danger of ML is not so much X-risk or malevolent AGI, but dumb ML being used inappropriately. And in general,…
> This is, as a ML researcher, exactly where I'm at (in belief). As an ML researcher myself, I'm glad to see other researchers maintaining a level-headed approach amidst the noise.
Well if you care more about the study than the money (which is nice -- though I'm at the tail of grad school), it makes more sense to chase knowledge than chase metrics. Then again, I don't come from a computer science background, so maybe not having that momentum helps.
Re: An Introduction to Statistical Learning with Applications in Python
#64Off topic, but it's very interesting to observe the ratio of upvotes / comments. On any given chatGPT topic, there are hundreds of comments usually. Here, so far 100 upvotes, only 7 comments. The book looks great - and given the authors, it almost certainly is (I will buy it for sure). It makes me think though about the state of 'ML / AI / Data Science' - and the cynic part of me thinks that this upvotes / comments r…
As a ML researcher, I don't think you're far off the point. w.r.t HN, there's almost all hype and no "science". People have strong convictions but not strong evidence. They happily cite papers, but only read the abstracts and miss the essential nuance. Especially in a field where suggesting limitations puts you at high risk of rejection (reviewers just copy paste that and thank you for the work). w.r.t academia, it i…
>don't know the difference between likelihood and probability. Similarly ones that don't understand probability density.
I'm a phd student in a "top university", in a research group primarily focused on data science (NLP, LLMs, blah blah blah). I'm 100% sure I am the only person in the group of ~25 (including profs/postdocs) that knows the difference between f(θ|x) and f(x|θ). In fact I'm pretty sure I'm the only person that has ever even seen f(θ|x) (because I took a stats sequence out of casella+berger). This group puts out dozens of papers a year. My research focus is not data science (compilers).
Re: An Introduction to Statistical Learning with Applications in Python
#65Re: An Introduction to Statistical Learning with Applications in Python
#66Earlier quoted context omitted.
This is pure gatekeeping. The math behind LLMs, that is, the math behind Neural Nets, is undergrad freshman level Calculus and some linear algebra. Not really complex at all. Can you deal with derivatives, the chain rule and matrix multiplications? Great you know all the "math" behind Deep Learning.
> Can you deal with derivatives Linear Algebra is a huge ... space? And then there's all the other algebras. I'm not sure what you are thinking, like, do you not know matrix multiplication you had better get a clue .
What does that have to do with ML/DL? You don't need Lie Algebra to understand how to do a matrix dot product...
Re: An Introduction to Statistical Learning with Applications in Python
#67Earlier quoted context omitted.
This is pure gatekeeping. The math behind LLMs, that is, the math behind Neural Nets, is undergrad freshman level Calculus and some linear algebra. Not really complex at all. Can you deal with derivatives, the chain rule and matrix multiplications? Great you know all the "math" behind Deep Learning.
That's the beginning math, but definitely not "the math behind LLMs". That includes probability theory, metric theory, topology, and more. But most people don't even acknowledge this, but then again, unless you're deep in a subject you don't really know the complexities of that subject. Red flags should go off whenever anyone says "it's just " or calls something simple. It's like the professor saying the proof is tri…
People like you are hilarious. You're sitting high in your ivory tower thinking that no one without a PhD in CS from Stanford/Berkeley/MIT can do what you do. Meanwhile people will take the Fast.ai course and be training full llm's from scratch in 6 months all the while you moan that "they don't even understand the REAL math". Yawn.
Re: An Introduction to Statistical Learning with Applications in Python
#68Earlier quoted context omitted.
As a ML researcher, I don't think you're far off the point. w.r.t HN, there's almost all hype and no "science". People have strong convictions but not strong evidence. They happily cite papers, but only read the abstracts and miss the essential nuance. Especially in a field where suggesting limitations puts you at high risk of rejection (reviewers just copy paste that and thank you for the work). w.r.t academia, it i…
>don't know the difference between likelihood and probability. Similarly ones that don't understand probability density. I'm a phd student in a "top university", in a research group primarily focused on data science (NLP, LLMs, blah blah blah). I'm 100% sure I am the only person in the group of ~25 (including profs/postdocs) that knows the difference between f(θ|x) and f(x|θ). In fact I'm pretty sure I'm the only per…
Well your name says something about who you are (and might mean you can guess at the roots of mine :). I often find PL people are more likely to have good math chops because it is taken seriously in their field.
Fwiw, at CVPR last year I asked every author of a diffusion paper about likelihood or score and only 2 gave me meaningful answers (1 compared their model's density against the data's density which was estimated through an explicit density method. Yeah, parametric vs parametric, but diffusion is not a tractable density method). It is really impressive that people who are working with probability and likelihood every day do not understand the difference (I see many assume they are the same, not just not know the difference).
Re: An Introduction to Statistical Learning with Applications in Python
#69Earlier quoted context omitted.
That's the beginning math, but definitely not "the math behind LLMs". That includes probability theory, metric theory, topology, and more. But most people don't even acknowledge this, but then again, unless you're deep in a subject you don't really know the complexities of that subject. Red flags should go off whenever anyone says "it's just " or calls something simple. It's like the professor saying the proof is tri…
People like you are hilarious. You're sitting high in your ivory tower thinking that no one without a PhD in CS from Stanford/Berkeley/MIT can do what you do. Meanwhile people will take the Fast.ai course and be training full llm's from scratch in 6 months all the while you moan that "they don't even understand the REAL math". Yawn.
If you want, you can get all of that math at any Top500 university, even in undergrad. I agree that people will be having impact and deploying these models without that understanding, but you don't need to be at Stanford to gain that understanding and it's something desirable if you want to do research instead of deployment.
Re: An Introduction to Statistical Learning with Applications in Python
#70Earlier quoted context omitted.
There's a meme where it shows someone stepping over all the steps to understand how to think about and analyze data directly to BERT. Well now people are stepping past BERT to stable diffusion and ChatGPT. It's been like this for years. Most work environments suffer from it in a bad way. I don't envy practicing data scientists managing expectations.
Interesting seeing job postings wanting 5+ years of LLM experience. Like unless you were at OpenAI working on GPT-1 or Google on BERT there's no one else in the world with that much experience and your shitty Startup/Fortune 500 company can't afford them anyway.
>Interesting seeing job postings wanting 5+ years of LLM experience.
Not really interesting.
Similar to seeing job postings some years back (and even recently), wanting n+ years of Rails experience when DHH had created it significantly less than n years before.
It's almost a meme on HN at this point.