Reader reviews

A useful guide. The examples assume a particular environment, but that is fixable.

This is the third book I have read on ai & machine learning this year, and it is comfortably the best of them. The balance between theory and practice is well judged; neither side dominates. The writing is direct and does not waste space on filler, which I value in a technical book. A couple of chapters felt like they could have been merged, but that is a minor complaint. Worth picking up if you already have the basics. Set realistic expectations and you will get a lot out of it.

Mixed feelings. The good parts are genuinely good; the rest is forgettable.

I picked this up after getting stuck on a project and it turned out to be exactly the right book at the right time. I particularly liked the way the author builds up from simple cases before introducing the harder material. A couple of chapters felt like they could have been merged, but that is a minor complaint. Some of the diagrams are dense and I had to reread a few sections, though that may just be me. Borrow it first if you can, or at least check the table of contents carefully before buying.

I have read a lot of books on this subject and this is the clearest.

I read this over a couple of weekends alongside a small side project, which turned out to be the ideal way to approach it. The early chapters move quickly through material I already knew, but the later sections were genuinely new to me. It is denser than the page count suggests, and I found myself slowing down around the middle. If you have the prerequisites, this is an easy recommendation. I will be keeping it on my desk rather than the shelf.

Outstanding. I kept finding reasons to come back to it.

I bought this on a whim after seeing it mentioned in a forum thread, and I am glad I did. I particularly liked the way the author builds up from simple cases before introducing the harder material. The index and cross-references are surprisingly good, which matters for a book I will come back to. I particularly liked the way the author builds up from simple cases before introducing the harder material. If you have the prerequisites, this is an easy recommendation. I will be keeping it on my desk rather than the shelf.

Recommended, with the caveat that it is not a gentle introduction.

I was looking for something that would bridge the gap between tutorials and proper reference material, and this does that well. It is denser than the page count suggests, and I found myself slowing down around the middle. The balance between theory and practice is well judged; neither side dominates. Recommended, with the caveat that it is not a gentle introduction.

Worth every page. I have already recommended it to two colleagues.

I have been working with ai & machine learning for a few years now and I still found plenty here that was new to me. Some of the material is available elsewhere, but rarely presented this clearly. I appreciated the honesty about what the book does not cover — it saved me from expecting things that were not there. The explanations are careful without being condescending, which is a difficult balance to strike. An easy five stars from me. I look forward to whatever the author writes next.