Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks

Really like this book explaining the state of the art in Neural Networks. I first encountered Jeff Heaton's work when i was looking for a neural network to predict forex on the mt4 platform, we used encog at that time. Jeff does not only have a high level view over neural networks, he knows also how to build them from ground up.

Definitly worth its price for the content and the GitHub examples. The content is pretty minimalistic in of itself and is insufficient to have a full understand of deep learning, but there are some really good pointers and simplified explanations to help you get there. I enjoyed the fact that each concept was presented seperatly which helped me get a better undestanding on how neural networks works and what is there underlying philosophie.

Jeff does an excellent job. The book is a little more academic then I would have liked as I normally like to see practical applications as well but his github/class supplements that information fantastically.

The book gives a brief description of the large number of types and techniques for applying neural networks, but never gives the reader any examples of their implementation and how effective they are at solving any of the classic problems. Instead the reader is fed trivia like the definitions of sgn(x), mean, standard deviation, partial derivative or the amazing fact that the second derivative is the derivative of the first derivative. There is nothing like a tutorial anywhere in the book.

I needed to understand the concepts for my job, and come up with ideas in machine learning. This book was a great overview and an addition to my references in machine learning.

Excellent introduction! Would highly recommend to anyone jumping in to the "deep" end ;)

The book is more like a quick compilation of a college student's note. Concepts are presented in a relatively isolated manner; connections between concepts are, for the large part, missing. Furthermore, if the materials presented are rather shallow like in this book, readers will expect to see a strong emphasis on, or hands on exercises of, practical applications. But this book doesn't seem to help much in that regard either, despite what the book claims. The book does give introduction to a bunch of models, which can be useful for a beginner. But at least this edition I wouldn't suggest any one to buy because of poor editing. For example, the pseudo code fragments listed in the book are often wrong, or different than what is described by the text. References to formulas, etc need some proof reading, too.

Simple introduction. But examples could be unclear. Eg when introducing convolution layers, lack of explanation on the choice of dimension.

This is a subject I have been personally researching and reading from a number of texts, both for programmers and for the general interest. Jeff Heaton's book help to under-cut many missed details in other types of texts. As I work through this book I feel I have the tools to now design and build my own artificial neural network to my requirements rather than ploughing through recipes in cookbooks that left wanting in the dark on a lot of details, unable to judge how to best to manipulate the pieces and components. This is not an advanced text for the professional; this is very much about building solid foundations to understanding. A thoroughly great book.

Many equations presented in here are incomplete, a few of them are simply wrong. While some basic concepts are explained well, most of really important ideas are skimmed through. In short - just as many other reviewers noted - terrible for beginners, useless for professionals.

Great book!

Quite undigestible course, though I'm already keen on such a subject !! Bad Blend of lots of concept without mathematical explantions. It is a pitty with such a tilte...

Tutto OK