You are here

Technology

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. 

Calais Document Category: 
Company: 
Technology: 

Chapter 5: Propagation Training

Calais Document Category: 
Position: 
Technology: 
Company: 

Chapter 2: Building Encog Neural Networks

Calais Document Category: 

Chapter 1: Introduction to Encog

Calais Document Category: 
Programming Language: 
Company: 

Applying Multithreading to Resilient Propagation and Backpropagation

This article shows how the Multi Propagation (MPROP) algorithm was implemented for Encog for Java. Though this article focuses on the Java implementation the C# version would be very similar. MPROP is based on resilient propagation, but is designed to work well with multicore computers and gain maximum performance.

Calais Document Category: 
Events Facts: 
Programming Language: 
Company: 

Programming Contributions

Are you a Java or C# programmer? Would you like to contribute some time to the Encog project? We are always looking for volunteers, and at all skill levels. You do need to be proficient with Java or C#, but you by no means need to be an AI expert. We are, of course, glad to have AI experts help! But we usually have tasks available that do not require advanced knowledge of AI.

Suggesting New Features

Calais Document Category: 
Programming Language: 

Building with Layers and Synapses

    You are now familiar with all of the layer and synapse types supported by Encog. You will now be given a brief introduction to building ANNs with these neural network types. You will see how to construct several neural network types. They will be used to solve problems related to the XOR operator. For now, the XOR operator is a good enough introduction to several neural network architectures. We will see more interesting examples, as the book progresses. We will begin with the feedforward neural network.

Calais Document Category: 
Technology: 

Using a Neural Network

    We will now look at how to structure a neural network for a very simple problem. We will consider creating 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 sort of the “Hello World” application for neural networks.

Calais Document Category: 
Technology: 

Choosing the Best C# Array Type for Matrix Multiplication

Implementing 2D arrays in C# involves some decisions as to how to represent the array. C# has two different types of arrays. This is somewhat different than the Java programming language, which supports only a single type of array. In C# you must choose between rectangular and jagged arrays. There are very important considerations for each of these array types. There are also many articles about the differences between array handling in Java and C#. This article focuses on one thing-- performance. Particularly how to implement a matrix with the best performance.

Calais Document Category: 
Programming Language: 
Technology: 
Events Facts: 

Pages

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer