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You can download the whole book (legally) from here [pdf]: https://hastie.su.domains/ISLP/ISLP_website.pdf
That is out of date. The link you put is literally in OP's link but 2nd edition.
Personally, I prefer Bishop (Pattern Recognition and Machine Learning) or Murphy (Probabilistic Machine Learning: An Introduction).
Earlier quoted context omitted.
That is out of date. The link you put is literally in OP's link but 2nd edition.
I'm not certain, but looking at that site it appears that the 2nd edition is the R version, whereas there is only one edition of the Python version.
Personally, I prefer Bishop (Pattern Recognition and Machine Learning) or Murphy (Probabilistic Machine Learning: An Introduction).
I heard good things about Bishop however I am a SE that would like do know more about what the ML team is doing and maybe work on some ML side projects. Would you recommend Bishop here or is it considerer to theoretical for such a case?
The python version is great news. I get asked fairly frequently to recommend an intro ML book. I would have suggested this, except they usually only knew python and not R. Now it a perfect first book!
Good ol days of ML
I don't think most people realize this but the "old" stuff often works better, has less churn, and has far lower overhead costs for deployment than the "new" stuff. Depends on the domain and the goal.
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…
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 is a bit better, but I find that in general there are a lot of researchers missing math fundamentals. I know or have met people at top universities or top labs that don't know the difference between likelihood and probability. Similarly ones that don't understand probability density. Even ones working on diffusion. But I will say, that in general the most prominent researchers do have these skills. But you'll notice that they aren't publishing as fast and their works might not even be as popular. A lot of research right now goes into parameter tuning and throwing compute at the problem. I've been a bit vocal about this though. Mostly due to it being a barrier to other types of research (because I'll admit that the tuning is needed, but we need to be honest that it isn't high innovation either and that it is hard to prove these are better given that we haven't tuned other models/architectures to the same degree).
tldr: You're pretty spot on. There's a shit ton of noise in ML/AI. Especially on HN
Edit:
I thought I should also suggest Richard McElreath's Statistical Rethinking (https://xcelab.net/rm/statistical-rethinking/), which is a more enjoyable read than ISLR and will also introduce you to Bayesian stats (Lectures are also on youtube). I'd also suggest Gelman's Regression and Other Stories (https://avehtari.github.io/ROS-Examples/).
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…
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…
> 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, that is using ML without understanding the limitations and having checks on it to ensure that when hallucinations happen that they don't cause major problems. But we're headed in a direction where we're becoming more reliant upon them and then once we have a few big issues with hallucinations the bubble will burst and can end up setting us back a lot in our progress to creating AGI. Previous winters were caused by lack of timely progress, but the next winter will happen because we shoot ourselves in the foot. Unfortunately, the more people you give guns to, the more likely this is to happen -- especially when there's no safety training or even acknowledgement of danger (or worse, the only discussion is about being shot by others).
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…
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.