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Introduction to Neural Networks for C#, 2nd Edition Paperback – 2 Oct 2008

3.5 out of 5 stars 2 customer reviews

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Product details

  • Paperback: 428 pages
  • Publisher: Heaton Research, Incorporated; 2 edition (2 Oct. 2008)
  • Language: English
  • ISBN-10: 1604390093
  • ISBN-13: 978-1604390094
  • Product Dimensions: 19 x 2.5 x 23.5 cm
  • Average Customer Review: 3.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 715,751 in Books (See Top 100 in Books)

Product Description

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.

Customer Reviews

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Top Customer Reviews

Format: Paperback Verified Purchase
First off - the positives.

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 The code is horribly outdated (the book was released in 2009, so there's no excuse)- for example not using operator overloading instead of "Add" and "Multiply" static methods in a "Helper"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.
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Format: Paperback
I agree with N Danson's review to a degree, but more with DiJuMx's review of the java version http://www.amazon.co.uk/Introduction-Neural-Networks-Java-Second/dp/1604390085/ref=sr_1_2?s=books&ie=UTF8&qid=1396803845&sr=1-2&keywords=neural.

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.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 3.6 out of 5 stars 8 reviews
12 of 12 people found the following review helpful
5.0 out of 5 stars Excellent book and supporting classes 10 Mar. 2009
By Sara Morgan Rea - Published on Amazon.com
Format: Paperback
I really like the way this guy has gone about breaking down a very difficult topic and making it easily digestable. He steps you through the basics of neural networks in a way that no programmer could not understand. But the best part of the book is the fact that Jeff offers online courses, in which he talks you through the important aspects of the book. Now, Jeff is not the best speaker in the world, but the fact that he reviews the book really helps to solifidy what you are learning.

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.
13 of 15 people found the following review helpful
3.0 out of 5 stars Necessary practical review of methods for NNs, needs polishing 17 Nov. 2009
By SDB Mike - Published on Amazon.com
Format: Paperback Verified Purchase
The author provides a needed introductory level book for NNs. I have several theoretical books on my shelf that hit me like a brick wall. Sifting through code with accompanying explanatory information is a luxury for non-theoretical folks like myself. Sadly, physically crunching the numbers (okay, letting code do it) while monitoring code execution is what some of us need to get a mathematical idea to sink in. This book provides that.

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.
5 of 5 people found the following review helpful
5.0 out of 5 stars Amazing book 6 Jan. 2009
By Eddie - Published on Amazon.com
Format: Paperback Verified Purchase
The book explains everything in perfect detail, starting at the most basic level and finishing with advanced techniques. Source code is provided AND Maintained, with libraries open source and public, so you're getting more than what you paid for compared to most other books like this.

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.
5.0 out of 5 stars A very nice and easy to read introduction 18 Feb. 2013
By JoshK - Published on Amazon.com
Format: Paperback Verified Purchase
The author did a great job. This book is simple to read and the examples are very straightforward. You don't really need much beyond basic algebra to get engaged and to learn something basic. I've read a few books on AI and they are usually from the standpoint of linear algebra, PCA, logit regressions, etc. This is from the standpoint of a programmer without the formal math. It's not a ton of information - it is just an introduction, but he did a great job.
2 of 2 people found the following review helpful
4.0 out of 5 stars Practical introduction to Neural Networks in C# (plus mistakes) 29 Sept. 2015
By Thomas Wikman - Published on Amazon.com
Format: Paperback Verified Purchase
This is a practical introduction to Neural Networks using the C# programming language. The book is filled with examples and implementations and the corresponding code is listed in the book and on his website. Unfortunately I saw no zip file or other downloadable archive so you have to copy and paste the code. The book discusses the simple Hopfield network and the standard feed forward back propagation networks, self organizing networks (Kohonen), as well as genetic algorithms and simulated annealing used independently and in conjunction with neural networks. The book covers matrix algebra and pruning of neural networks and some interesting applications such as predictive neural networks (financial prediction), OCR, and Bot programming. I thought the chapter summaries and review questions were quite helpful. The book also features useful appendixes and a useful glossary (and index).

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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