Comment by kingkongjaffa | Hacker News Reader

Comment by kingkongjaffa | Hacker News Reader

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Heres a review from one of the other books by pakt and this author:

If you are an inexperienced programmer or new to Python/Jupyter/Anaconda DO NOT BUY THIS OR ANY OTHER PACKT Publishing book as the code contains errors that are difficult to rectify. Packt Publishing DOES NOT verify code like the CRC Press - for instance, Statistical Rethinking by CRC press - i.e. Bayesian Analysis in R - has code AND excellent content that is helpful for both the academic and practitioner. Also, the Packt Publishing description/outline of probability is weak, at best, and is confusing to many of my students.

If correcting Python code is not a big deal for you then Packt books are a nice intro. But why buy something that you have to fix before you can start working with it?

This is a December 2018 update - DO NOT BUY THIS OR ANY OTHER PACKT PUBLISHING BOOK UNLESS YOU CAN VERIFY THERE IS AN ERRATA FILE TO ACCOMPANY IT. PACKT PUBLISHING DOES NOT PRODUCE RELIABLE TEXTS. MY STUDENTS HAVE HAD A TERRIBLE TIME WITH THIS BOOK – THE COMPANY HAS NOT RESPONDED TO ANY QUESTIONS/REQUESTS - THE UNIVERSITY WHERE I TEACH IS NOW LOOKING CLOSELY AT THE VALIDITY OF THESE PUBLICATIONS.

I want to second the recommendation for Statistical Rethinking from that review, it’s one of the best books ever written on computational Bayesian inference.

However, it is R based, and therefore arguably not an alternative to the book we are discussing here.

As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:

https://github.com/pymc-devs/pymc-resources

(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo (https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)

Agreed - that book is phenomenal at communicating the theory and mindset of Bayesian analysis. My favorite quote from the lectures - "Statistics should be subbordinate to Science." A very healthy emphasis on logic and proper model design - there will be no shoving data into black boxes!

R is a good language for this but he uses a library of convenience functions that are not on CRAN and are effectively just for educational purposes. So even if you want to stick with R, you'll need to translate your code into production-ready libraries anyway. There are several nearly-complete translations based on other R packages as well as ported to Julia and Python.

Or you could just put his code into production (source: I worked at a super successful gaming startup that had this code scattered all over their repo).

They had a bunch of other code problems too, but that was definitely the weirdest thing I saw in my (very short) stay there.

I thought the whole paragraph about "This package is never going to be on CRAN" was a warning label :-)

I agree, it works beautifully and performance is ok for desktop use.

You are cheating yourself if you only read the book. His lecture series covering the material on Youtube is phenomenal.

I went through the book + season 1 videos, and had a glance at some of the season two videos. The season two has some visuals that made some intuition click for me.

Now I see he has a 2023 playlist, which may yield even further improvements: https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XH...

I actually took his college course the YouTube videos are lecture recordings of

It is not just code. Content in general is horrible, too. You put together a few badly written blog posts and it becomes a Packt published book.

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