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Introduction to Neural Networks for C#, 2nd Edition Paperback – 2 Oct 2008
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About the Author
Jeff Heaton is an author, consultant, artificial intelligence (AI) researcher and former college instructor. Heaton has penned more than a dozen books on topics including AI, virtual worlds, spiders and bots. Heaton leads the Encog project, an open source initiative to provide an advanced neural network and bot framework for Java and C#. A Sun Certified Java Programmer and a Senior Member of the IEEE, he holds a Masters Degree in Information Management from Washington University in St. Louis. Heaton lives in St. Louis, Missouri.
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Top Customer Reviews
Coming into this book I had little knowledge of neural networks. I knew a little about how they were supposed to work, but with no real background trying to write one from scratch was an intimidating task. After leaving this book, I understand the concepts a lot better, and would be able to understand well written code in other peoples neural networks. But I think the broad reality is that writing one from scratch is always going to be challenging. But I understand the concepts, and neural network types and training methods, etc to know what kind of network I'd need to use in a given scenario, and how to train (or not) it.
The negatives? Well its clearly a very hastily written port of the Authors own "Introduction to Neural Networks for Java". The code is horribly outdated (the book was released in 2009, so there's no excuse)- for example not using operator overloading instead of "Add" and "Multiply" static methods in a "Helper" class? No use of extension methods instead of the obligatory Helper classes to aid readibility? And there were a few cases where a hint of LINQ would make the code easier to read, more maintainable and potentially more efficient. Now I know this isnt a "Teach C#/LINQ/whatever book", its a "Teach Neural Nets" book, but to qualify it with "for C#" means you should be using appropriate language features, and not lazily porting line by line from a Java codebase.
Add to that the formatting errors that appear all over the place (using Console.Read more ›
The book starts with basic explanations based on neurons that fire when the weighted sum of inputs exceeds a threshold. However it keeps on using the term threshold later when it's moved on to neurons that use continuous output scaling functions without explaining the switch. There's no significant explanation of the back propagation process. The text explains the first level of program, that calls on the classes provided on the web site, but does not explain how those classes are structured or operate. There is a Tic Tak Toe and a "predict the S&P500" example, but mostly the neeural net examples are exclusive OR or visual pattern recognition. The examples report their final error after training, but not the weight values selected, which seemed odd to me.
You'll have to work to get an understanding the implementation and theory of neural nets, but if you want working programs, and a framework that allows you to build your own nets without needing a deep grasp of the theory, its a reaonable choice; most alternatives are either all maths and theory, or C++, as far as I can see.
I wanted to learn how NN programs work, to transfer the knowledge to a different environment, and have spent about a week trying to figure that out from the bottom up by single-stepping through the code. Its possible, but hard work, and it could have been so much easier with some documentation of the library, say in an appendix.Read more ›
Most Helpful Customer Reviews on Amazon.com (beta)
I am working through the book and after I have read the chapter, I go back and watch the videos. I feel like I am really getting all this. I have looked at neural network books before, but none of them have explained everything in detail the way this guy is. I highly recommend this book.
That being said, the author writes very mechanically, bordering on robotic. Just a little more writing finesse would greatly improve the readability. The text is still quite readable, though, and the references are available to dig deeper (Neural Smithing, for instance). In addition, for folks interested in the code architecture, this book is sadly lacking. Theoretical and mathematical information is first presented, followed by most of the code, and then code walkthrough. The code walkthrough is somewhat helpful and necessary, but discussion of the how and why of the code implementation is not adequately addressed. A few choice UML diagrams would greatly improve the reader's understanding of the code flow. This is especially important for the feedback sections.
Now, I bring this up because I think it is vital to an avid reader: I have only read the first 7 chapters. What? Really? And yet I am willing to rate it? Yes. It is the critique above that has kept the book sitting on my shelf the past several weeks. This is very interesting and directly applicable material and I should be willing and ready to devour it... and yet, I'm not. I will garner the will power to continue to the end because the Bot section was the content that queued me to by the book. Making learning agents, coupled with Buckland's approach in Programming Game AI by Example, is quite valuable to me. I think a 3rd edition could become a coder's gem.
I own one other book by Heaton and he does the same there too, so I can safely say that this man understands what software developers are REALLY looking for. My new favorite author.
The examples were illustrative and useful and the book was well organized. The writing style was a bit mechanical but the book was otherwise well written and fairly clear. Unfortunately it seems to contain some typos in the math and equations and that will confuse you a lot more than a typo in the text would. In chapter two page 63 the rows and columns in the weight matrix seems mixed up and on page 278 there is dot product that is completely wrong. The top of page 168 features a paragraph that is complete BS but it did not deter from understanding any of the important content.
Overall I think this is a very useful introduction to Neural Networks using C# and I recommend it, at the same time as I wish the code was more easily downloadable and that there were no mistakes in the math.
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