And I’m surprised to not find Aurelion Geron’s absolute masterpiece listed below. I believe it is the best machine learning book ever, although Statistical Learning mentioned in the article is really good as well :
The Goodfellow book is not complete as an academic intro, but no one book can be. It's not very useful as a practical tutorial, but no book seeking this could cover the mathematical arguments that Goodfellow's book does. I found Goodfellow's book extremely useful for consolidating a lot of handwaving that I'd seen elsewhere and putting it in a slightly more rigorous framework that I could make sense of and immediately work with as a (former) mathematician.
Goodfellow's treatment is especially useful for mathematicians and mathematically-trained practitioners who nevertheless lack a background in advanced statistics. The Elements of Statistical Learning, for instance, is extremely heavy on statistics-specific jargon, and I personally found it far more difficult to extract useful insights from that book than I did from Goodfellow's.
So, no amount of praise and mathematician's justification makes sense. I agree that it is inclined for mathematicians, but this book is overrated and it is terribly due for a rewrite, update and frankly in my personal view - the writing style.
I am curious of specific parts of the book you found valuable.
By comparison, Goodfellow and his co-authors seemed to just dump everything they know onto the page. It's fragmented, bloated, and it meanders all over the place. Goodfellow was on a recent podcast where he seems to acknowledge that the book straddles an awkward place between tutorial and reference.
I don't mean to sound too harsh. I appreciate its scope, and I've certainly read much worse textbooks.
If the worst you can say is it's not a classic text, that's really not saying much at all. I feel weird defending the book so much when to me it's just a book I found useful and I don't even feel that strongly towards it. But the strength of some of the criticism here doesn't seem motivated by the book itself.
A reference implies you already know the topic and just want an index to jog your memory for things you can't hold all in your head at once.
That is different than a pedagogical tool. If so, you shouldn't recommend it to those want to learn the topic.
I agree with the poster below. Outside of classes, lecture notes, the books I listed, and Sutton/Barto (Intro. to Reinforcement Learning) have taught me the material. I use Goodfellow to brush up before interviews or jog my memory about topic I don't work with very often (like computer vision).
- Hastie is a co-author of two machine learning books, one is "Elements of Statistical Learning" which is very comprehensive, and "Introduction to Statistical Learning", which is more approachable by people without too much background in stats.
The reason for Goodfellow's popularity is that it was publish in 2014 right at the turn of exponential interest in Deep Learning after AlexNet. It took off and became popular, but readers now feel it is stale for the aforementioned reasons.
I've always thought that Hands on ML by Geron was great implementation wise, but lacking in the mathematical rigor and depth. While I would have a general sense of what is going on after reading it, and I'd certainly be able to structure and implement a model, I don't know if I would have any deep intuitions.