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For what it's worth, I disagree quite strongly with that review. The book is aimed at those with a pretty mature appetite for abstract mathematical reasoning, b
by nilkn 8y ago
For what it's worth, I disagree quite strongly with that review. The book is aimed at those with a pretty mature appetite for abstract mathematical reasoning, but not much specific knowledge in the areas of statistics, machine learning, and neural networks. It's an actual graduate-level book, and one must approach it with the appropriate background and education.
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.
- spectramax 8y agoThe problem with Goodfellow's book is that it is half-baked. I don't know why he has to introduce Linear Algebra section for a 3rd of the book (which ends abruptly) but then moves on to the ANNs. If Goodfellow intended this book for mathematicians, that whole section about LA can be omitted with literally no loss in the book's value. The whole book arguably feels rushed. 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.
- nilkn 8y agoThe section on linear algebra moves quickly. I don't see how a book including extra prerequisite material is an example of it being half-baked or rushed. Surely that would actually be an example of the opposite? That section is a nice reference to have immediately available for things like SVD and PCA.
- jhanschoo 8y agoPerhaps restructuring the LA part into an appendix would be preferable. For mature readers, it nevertheless serves as a way to focus and agree on notation that is used for the rest of the book.
- freyr 8y agoThe best textbooks, the ones considered classic gems in their field, are careful about what they say and what they leave out. You get the sense that every word is in its right place. 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.
- nilkn 8y agoConsidering how much is not in the book -- and that the book is not even that large as far as textbooks go -- part of me feels this criticism is somewhat disingenuous. I'd agree that the direction of the final part on research topics feels fragmented, but the rest of the book certainly doesn't. It's very clearly focused on developing deep neural networks and doesn't actually meander at all. 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.
- deleted 7y ago[deleted]