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Book is 10 years old, isn't it outdated?
Have not read the book, but only deep learning has had such wild advancement that a decade would change anything. The fundamentals of ML training/testing, variance/bias, etc are the same. The classical algorithms still have their place. The only modern advancement which might not be present would be XGBoost style forests.
Book is 10 years old, isn't it outdated?
The updates to even the Bias/Variance Dilemma (Geman 1992) are minor if you look at the original paper:
https://www.dam.brown.edu/people/documents/bias-variance.pdf
They were dealing with small datasets or infinite datasets, and double decent only really works when the patterns in your test set are similar enough to those in your training set.
While you do need to be mindful about some of the the older opinions, the fundamentals are the same.
For fine tuning or RL, the same problems with small datasets or infinite datasets, where concept classes for training data may be novel, that 1992 paper still applies and will bite you if you assume it is universally invalid.
Most of the foundational concepts are from the mid 20th century.
The availability of mass amounts of data and new discoveries have modified the assumptions and tooling way more than invalidating previous research. Skim that paper and you will see they simply dismissed the mass data and compute we have today as impractical at the time.
Find the book that works best for you, learn the concepts and build tacit experience.
Lots of efforts are trying to incorporate symbolic and other methods too.
IMHO Building breadth and depth is what will save time and help you find opportunities, knowledge of the fundamentals is critical for that.
Book is 10 years old, isn't it outdated?
I would recommend https://udlbook.github.io/udlbook/ instead if you're looking to learn about modern generative AI.
Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
[1]: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw
Book is 10 years old, isn't it outdated?
I have read parts of it years ago. As far as I remember, this is very theoretical (lots of statistical learning theory, including some IMHO mistaken treatment of Vapnik's theory of structural risk minimization), with strong focus on theory and basicasically zero focus on applications. Which would be completely outdated by now anyway, as the book is from 2014, an eternity in AI. I don't think many people will want to…
Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
I haven't read that book, but I can personally attest to Josh Starmer's StatQuest Youtube channel[1] being awesome! I used his lessons as a supplement to my studies when I was studying statistics in uni. [1]: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw