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Programming Neural Networks with Encog 3 in C# [Paperback]

Jeff Heaton
4.0 out of 5 stars  See all reviews (1 customer review)
Price: 21.55 & FREE Delivery in the UK. Details
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Book Description

3 Oct 2011 1604390263 978-1604390261
Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classifiction and image processing are introduced.

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Product details

  • Paperback: 240 pages
  • Publisher: Heaton Research, Inc. (3 Oct 2011)
  • Language: English
  • ISBN-10: 1604390263
  • ISBN-13: 978-1604390261
  • Product Dimensions: 1.3 x 18.8 x 23.1 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 596,238 in Books (See Top 100 in Books)

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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.

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Most Helpful Customer Reviews
4.0 out of 5 stars excellent book on neural nets 17 Aug 2012
Format:Kindle Edition|Verified Purchase
Great book, a very extensive library produced by Jeff Heaton

Only criticism if any is the lack of working examples, if there were more of those it would be perfect!
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Most Helpful Customer Reviews on (beta) 4.1 out of 5 stars  7 reviews
3 of 3 people found the following review helpful
5.0 out of 5 stars Excelent reference 6 Aug 2012
By harveybc - Published on
Format:Kindle Edition|Verified Purchase
It is very well written and is easily assimilated. The content covers everything from basic experiments in the workbench to using the framework in c#. Very useful for AI integration into projects. Recomended.
2 of 2 people found the following review helpful
4.0 out of 5 stars Excellent base overview 6 Sep 2012
By Richard - Published on
Format:Kindle Edition|Verified Purchase
Well, if you are a c# programmer (not even necessarly a pro) interested in machine learning, and mostly neural networks, this is for you. The basic topics are well presented, and I liked, how Jeff writes, but I was missing the advanced, deeper parts - even with the exapmles, creating something advanced requires a lot of time and a huge amount of trial and error. If there would be a second part of this book for deeper understanding, I would gladly buy it - this one was all right for its price too.
1 of 1 people found the following review helpful
4.0 out of 5 stars Repetitive book that covers a brilliant, powerful tool thoroughly, and repeats itself, repeatedly. 4 July 2014
By Ivy - Published on
Format:Kindle Edition|Verified Purchase
Encog3 is a powerful tool for building neural nets in C# and Java very quickly and easily. If you are looking to get into AI, or if you need to use a neural net in your project without the enormous overhead of rolling your own, you will want to get to know Encog. This book is a thorough introduction to the tool. How to set it up. How to use it. How it does, what it does. It's all you need to get started with Encog3.

The only thing I did not like was the obvious hand of a project manager in the editing. You know the type. You write a sentence like "This is the house that Jack built" and they read half a sentence and complain "'That Jack built' isn't a sentence. It should be 'Jack built that.'" Ultimately you end up with, "This is Jack's Jack-built house, built by Jack, who built the house that Jack built."

In this book, that takes the form of:

This section will detail how to structure a neural network for a very simple problem: to design a neural network that can function as an XOR operator. Learning the XOR operator is a frequent "first example" when demonstrating the architecture of a new neural network. Just as most new programming languages are first demonstrated with a program that simply displays "Hello World,"neural networks are frequently demonstrated with the XOR operator. Learning the XOR operator is a sort of the "Hello World" application for neural networks.

It's an unfair structure, because the book is brilliant once you get past the endless repetition, and unnecessary information (they explain the Boolean operators "and" and "or", to an audience sophisticated enough to be using neural nets). The engine itself is revolutionary. The code examples are clear and shockingly concise.
1 of 1 people found the following review helpful
5.0 out of 5 stars Heaton is a leader 30 Oct 2013
By Richard C. Leinecker - Published on
Format:Paperback|Verified Purchase
I am a software engineer. Therefore, I am really tough on any technical books that I buy. Heaton does not get a soft pass from me. But the fact is this: I have never read a better book that explained neural networks, and I have been studying them for 20 years.

Good job.
5.0 out of 5 stars Your portal into the world of neural networks 13 Aug 2014
By Michael - Published on
Format:Kindle Edition|Verified Purchase
Several previous commenters have praised Jeff on his concise writing style and how he conveys a complex topic in an easily digestible manner. Let me put it another way - about a week ago I knew almost nothing about Artificial Neural Networks. My math skills are pretty decent (I'm a programmer) but I'm not versed in calculus. Now, I'm only half way through the book and it already has managed to give me a running start on all the basic concepts involved in building and training ANNs. I'm able to read pertinent white papers now plastered with formulas which largely escape me - but I am able to grasp the underlying principles and overall idea. I wouldn't be able to fully explain to you how resilient back propagation works, however the basic concept behind is now clear to me. I am also able to differentiate between various propagation training techniques and when to choose what type of data normalization. And that is sufficient in order to get started with experimenting and training simple ANNs. But the icing on the cake for me was that Jeff is offering a downloadable API and workbench (Encog) on his website for FREE. Plus there are ample examples in the book, providing hands-on examples and training. This is not just a technical book - it's a guided journey offering non-mathematicians the opportunity to explore the exotic world of neural networks. Which is why I believe it deserved a five-star rating. The book should be called 'Everything You Always Wanted To Know About Neural Nets, And Never Dared To Ask'.
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