Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks Paperback – 28 Oct. 2015

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.

Good

I felt compelled to write this review when I'm only about halfway through the book because it's that brilliant. For anyone who's has the ability to understand mathematical concepts, but doesn't necessarily have the formal education in mathematical notation, especially those from a programming background, will really appreciate this book. I've read screens upon screens of literature on AI and ANNs on the internet and they've all been, quite frankly, baffling. If that person is you too, this book will demystify these techniques and make you wonder why you were ever so intimidated in the first place.

I have very mixed feelings about this book.Having read most of Heaton's papers, articles and seen most of his videos, and I have to admit I was disappointed.The pros:Covers a wide range of networks, a few training algorithms, and so on.If you're a student just starting on ANN, then this is a good book to get your self acquainted with the concepts.You may even be able to write your own network if you do some follow-up searching.The bizarre stuff:This book reads like an ode to Heaton. Was it really necessary to say that Heaton did this and that?For example at some point the book claims that Heaton introduced convolutional nets, which is of course, not true.Later they mention LeNet and rectify the earlier statement.Would a quality book allow for such claims? I leave that up to you to decide.The mediocre stuff:All source code samples are in Python. I would have preferred Pseudo-code, but there are some source code listingswhich you can use to see basic versions of algorithms.Math formulae are not thoroughly explained, which is what you would expect from a beginners book.The print quality is poor, in fact the book mentions heat-maps and colours, whilst the actual print is in Black and White!The bad stuff:Heaton barely scratches the surface.This book reads like an introduction or summary to what neural nets are.Some subsections are a paragraph long (!!!) and then move on to the next subject.It feels as if they are trying to fill pages, rather than provide substantial material.I guess for the price I shouldn't really complain, but you could have read an e-book or online tutorial and gotten the same information.Overall, treat this book as a very gentle and brief introduction and you won't be disappointed.

Good book about how we are biological machinery and slaves to the system and how artificial intelligence is related to the way we think, good mechatronics stuff and engineering of neural networks and how thought processes and decisions are made.

These books seems to be written more by a hobbist than a professional. They often say the same basic things several times, they lack depth, and lack inspriational applications. I don't recomend them to anyone. Sorry.

Finally a nice and not so full of math book about deep learning, it would be more cool but if the chapter about visualisation talk a little bit more then just the T-SNE, but everything else is really good

Delivery and quality good