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Neural Network

Initial Impressions of GPU Programming

In this article I will discuss some of the primary issues with GPU programming. This article will focus on how we implemented the first version of GPU support in the Encog Machine Learning Framework. This article focuses mainly on using OpenCL from Java or C#, however it is general enough to apply to any language that can commu icate with OpenCL.

A Non-Mathematical Introduction to Using Neural Networks

The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. And like many things, sci-fi writers have created a vast, but somewhat inaccurate, public idea of what a neural network is.

Nguyen-Widrow and other Neural Network Weight/Threshold Initialization Methods

Neural networks learn by adjusting numeric values called
weights and thresholds.  A weight specifies how strong of a connection exists
between two neurons.  A threshold is a value, stored on each neuron that either
adds or subtracts from the incoming weights from other neurons.  Training is
the process by which these weights and thresholds are adjusted to cause the
neural network to produce useful results. 

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Four Part Encog Silverlight Demo Released

Would you like to see Encog Silverlight in action? Silverlight works quite a bit like flash. Except you can program it in C#, without having to learn Flash. There is a version of Encog for Silverlight. You can see several examples, in full GUI, here.

http://www.heatonresearch.com/encog/benchmark.html

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Introduction to Neural Networks for C#, Second Edition

Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.

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Encog 1.0 for Java Released

Encog 1.0 for Java has been released. Encog 1.0 for C# will be released soon, and is currently in the works. For now, Encog 0.5 is the current C# release. The following is a list of features that were implemented in Encog 1.0.

  • All Neural networks should implement the Network interface
  • Package structure reorg
  • Allow the three neural networks to be persisted in an XML format
  • Unify the three neural network types into a single solution
  • Add an image based data source
  • Add a CSV based data source

Introduction to Neural Networks for Java, Second Edition

Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.

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Two New Neural Network Books Coming Soon

I am nearing the final stages of two new books. Introduction to Neural Networks for Java and Introduction to Neural Networks for C#. The Java book is the Second Edition to the original "Introduction to Neural Networks with Java". This is a considerable update to the original text. The covers for the two new books can be seen below. The Java book is expected to be out in September,2008. The C# book by January,2009. More information on these two books will be posted in the coming weeks.


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JOONE Updates for Introduction to Neural Networks with Java

There have been a few updates to JOONE since the publication of "Introduction to Neural Networks with Java". A future edition of the book will be updated with this information, but for now, this article updates the book.

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