BIG IDEAS.PAGE-TURNING IMPACT.

Strong content, uneven pacing. I would still recommend it.

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 author avoids over-explaining, which keeps the pace up but means you need to pay attention. The code samples assume a particular environment, which took a bit of fiddling to get running properly. A few of the examples could be more realistic, but the underlying points come across clearly. Good value for the price and worth the time it takes to work through properly.

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.