31–40 of 87 posts
I’ve only had the chance to look over the Python lab for a few minutes, but compared to what I remember from the R labs, it is much, much more involved and longer.
I know HN likes to complain about how difficult and confusing R is, but I think that it is an easier language for beginners or stat inclined people to start doing statistical work in.
/two cents
Off 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…
In that case it is hard to say anything of value other than an upvote.
ISL is the best intro-level textbook of classic ML methods. It‘s theory-oriented yet simple enough to appeal to a wide audience of students (with basic knowledge in stats, linear algebra, and coding). Having the examples only in R was a pain when teaching with it while using Python. I hope they‘ll now turn this into a series of Jupyter notebooks and distribute it through Colab or similar.
> I hope they‘ll now turn this into a series of Jupyter notebooks You can find them here: https://www.statlearning.com/resources-python
This is an update to a very popular text which was originally in R. Professors Hastie & Tibshirani are leading educators in statistical learning. They also have a video course following these notes in Stanford Online. Very highly recommended if learning theoretical aspects of classical ML
Personally, I prefer Bishop (Pattern Recognition and Machine Learning) or Murphy (Probabilistic Machine Learning: An Introduction).
Those books are in a completely different ballpark.
Personally, I prefer Bishop (Pattern Recognition and Machine Learning) or Murphy (Probabilistic Machine Learning: An Introduction).
I suppose that's what makes the ISLA being translated such a big deal. A sufficiently advanced student in ML/Statistical modeling doesn't really need code at all since it should be fairly trivial to translate the mathematical models into computational ones, and the ability to do so is a prerequisite to understanding these models in the first place.
Off 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…
Off 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…
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.
It can be watched without the book. The coding parts can be skipped. It has some insights missing in the book, and they've got an amazing mix of incredible technical talent and a great ability to distill and explain concepts.
Earlier 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.
Since they're probably using ChatGPT to generate these job reqs - this is only to be expected.