Reader reviews

A genuinely enjoyable read that also taught me a great deal.

I have been working with higher education for a few years now and I still found plenty here that was new to me. The author is unusually honest about where the simplifications break down, which I appreciated. The index and cross-references are surprisingly good, which matters for a book I will come back to. I found myself making notes in the margins, which is always a good sign. I have already recommended it to two colleagues and will recommend it to more.

Finally, a book that treats ai & machine learning with the care it deserves.

I was looking for something that would bridge the gap between tutorials and proper reference material, and this does that well. The balance between theory and practice is well judged; neither side dominates. The index and cross-references are surprisingly good, which matters for a book I will come back to. There is a section near the end that ties several earlier ideas together in a way I had not seen before. This is the book I wish had existed when I started working with ai & machine learning.

This is the one to get if you are serious about webgpu.

There is a particular kind of technical book that is dense without being difficult, and this is one of them. The exercises at the end of each chapter are worth doing — they caught a few misconceptions I did not know I had. I particularly liked the way the author builds up from simple cases before introducing the harder material. 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.

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

I had been on the fence about this one for a while, and I should have bought it sooner. A few of the examples could be more realistic, but the underlying points come across clearly. The author clearly knows the subject deeply and has thought hard about how to teach it. A couple of chapters felt like they could have been merged, but that is a minor complaint. I have already recommended it to two colleagues and will recommend it to more.

Well worth picking up if you already have the basics.

I have been working with computational biology for a few years now and I still found plenty here that was new to me. There is a section near the end that ties several earlier ideas together in a way I had not seen before. The book rewards careful reading. Skimming it would be a mistake. Four stars, and I suspect a second edition could easily push it to five.

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

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 terminology is introduced without much ceremony; I had to look a few things up along the way. The book rewards careful reading. Skimming it would be a mistake. I have already recommended it to two colleagues and will recommend it to more.