Reader reviews

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

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 exercises at the end of each chapter are worth doing — they caught a few misconceptions I did not know I had. The index and cross-references are surprisingly good, which matters for a book I will come back to. It is denser than the page count suggests, and I found myself slowing down around the middle. Worth picking up if you already have the basics. Set realistic expectations and you will get a lot out of it.

Decent, but uneven. Some chapters are much stronger than others.

There is a particular kind of technical book that is dense without being difficult, and this is one of them. A few of the examples could be more realistic, but the underlying points come across clearly. The progression from chapter to chapter is well thought out; nothing feels out of order. The exercises at the end of each chapter are worth doing — they caught a few misconceptions I did not know I had. Borrow it first if you can, or at least check the table of contents carefully before buying.

Excellent from start to finish. No filler, no padding.

This is the third book I have read on ai & machine learning this year, and it is comfortably the best of them. The index and cross-references are surprisingly good, which matters for a book I will come back to. The exercises at the end of each chapter are worth doing — they caught a few misconceptions I did not know I had. Worth the money and the reading time, without hesitation.

I got a lot out of this, even if a few sections felt rushed.

I came to this book already familiar with the basics, and it still had plenty to offer. The early chapters move quickly through material I already knew, but the later sections were genuinely new to me. The progression from chapter to chapter is well thought out; nothing feels out of order. The author avoids over-explaining, which keeps the pace up but means you need to pay attention. Solid, and I would buy another book from this author without hesitation.

Solid and well-organised. A couple of chapters dragged a little.

A colleague recommended this and I can see why. It does something that few technical books manage: it respects the reader's time. The chapters on ai & machine learning are the strongest — the examples are stripped down but complete, and I could follow along without constant guessing. A couple of chapters felt like they could have been merged, but that is a minor complaint. The author is unusually honest about where the simplifications break down, which I appreciated. A good addition to a technical library, as long as you know what you are buying.

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

I bought this on a whim after seeing it mentioned in a forum thread, and I am glad I did. A few of the examples could be more realistic, but the underlying points come across clearly. Some terminology is introduced without much ceremony; I had to look a few things up along the way. An easy five stars from me. I look forward to whatever the author writes next.