Fascinating World of Neural Nets  
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Introduction to KCM
Why Neural Nets?
Introduction to ANN
AI in Real World
Tools & Utilities
Blog Articles
Discussion Board
NNDef Toolkit
Knowledge Models
nBank Project


Introduction to Neural Nets
Basic introduction to Neural Networks including an animated presentation of different Networks learning process. Easy to understand.

Method and Instruments for Modeling Integrated Knowledge
This paper is to present a framework developed in order to model and share knowledge within large organizations whether they be private or public.
Called MIMIK (Method and Instruments for Modeling Integrated Knowledge), it is based on a methodology,on eight different models for graphical representation and on a knowledge-sharing system.

Introduction to predictive analytics
Predictive analytics encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future.

Face Recognition
Over the last ten years or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. This site provides relevant information in the the area of face recognition / Information pool for the face recognition community / Entry point for novices as well as a centralized information resource.

AI FAQ/general
Answer to certain questions and topics that come up frequently in the various network discussion groups

Answer to certain questions and topics that come up frequently in the Neural Nets discussion groups

Online Course Teaches Java and C# Neural Network Programming
Heaton Research introduced two new online video-based courses today. The Introduction to Neural Networks for Java and Introduction to Neural Networks for C# courses are now open for enrollment. These courses are offered free to the public. Programming experience in Java or C# is suggested. These courses are taught by Jeff Heaton, an artificial intelligence researcher and former college instructor. The courses are presented in fifteen weekly units consisting of a 20-30 minute video supplemented with additional materials, assignments, and an online forum.

FACETS - A recent EU-funded neural computing research project
The goal of the FACETS (Fast Analog Computing with Emergent Transient States) project is to create a theoretical and experimental foundation for the realisation of novel computing paradigms which exploit the concepts experimentally observed in biological nervous systems. The continuous interaction and scientific exchange between biological experiments, computer modelling and hardware emulations within the project provides a unique research infrastructure that will in turn provide an improved insight into the computing principles of the brain. This insight may potentially contribute to an improved understanding of mental disorders in the human brain and help to develop remedies.

International Neural Network Society
The International Neural Network Society (INNS) is the premiere organization for individuals interested in a theoretical and computational understanding of the brain and applying that knowledge to develop new and more effective forms of machine intelligence.

The Robot Report is a new website dedicated to tracking the business of robotics. It is a resource for news, info and links to and about this growth industry.

Introduction to Neural Networks
This article describes how to implement an artificial neural network
that is capable of being trained to recognize patterns.

Introduction to Genetic Algorithms
The site introduces some of the fundamentals of genetics algorithms. It is intended to be used for learning genetics algorithms without any previous knowledge of that subject.

Neural Network sample training data from UCI
This is a repository of datasets to be used for neural network training.
Covers more 10 different problem areas.

Introduction to Neural Networks
A well written introduction on artificial neural network and their applications.

Introduction to Neural Networks & Learning Methods
A very good introduction to artificial neural network and their learning methods.

Introduction to Neural Networks from Department of Defense
An excellent introduction to the basic principles of neural networks.

Introduction to Self-Organising Maps
An excellent introduction to the self-organized Maps and Pattern Recognition

Introduction to Support Vector Machine (SVM)
A short introduction to Support Vector Machine

Neural Networks Books
A total of 349 Neural Networks items sorted by poularity. Includes reviews, product descriptions, prices, and buying information.

The society for ANN in Medicine and Biology

A collection of links to people and organisations working with medical applications of artificial neural networks and related techniques.

The Kernel Machines

A Site dedicated to Kernel Machines and related methods. But also Gaussian Process prediction, Mathematical Programming with Kernels, Regularization Networks and Reproducing Kernel Hilbert Spaces.

Neural Network Companies

A Site will reference to companies with products based on Neural Networks.

Information on over 50 NN applications

BrainMaker Neural Network Software: Great list of examples of specific NN applications regarding stocks, business, medicine, and manufacturing.

Neural Networks Tutorial with Java Applets

This site includes a series of exercises and demos. Each exercise consists of a short introduction, a small demonstration program written in Java (Java Applet), and a series of questions which are intended as an invitation to play with the programs and explore the possibilities of different algorithms.

Neural Networks Introduction

NEURAL NETWORK DESIGN provides a clear and detailed survey of fundamental neural network architectures and learning rules. In it, the authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems.

Neural Networks Innovations and Patents

Recent U.S. patents related to Neural Networks

Neural Networks & AI for Gaming

Jay Scott resources site for Machine Learning for Gaming. He describes game programs and their workings; they rely on heuristic search algorithms, neural networks, genetic algorithms, temporal differences, and other methods.

NeuroEvolution of Augmenting Topologies (NEAT)

NEAT is a genetic algorithm for evolving neural networks written by Ken Stanley at University of Texas at Austin and published under the GPL.

SharpNEAT (C# implementation of NEAT)

NEAT is a technique for evolving neural net structure and connection weights, SharpNEAT is one of a number of implementations but happens to be written in C#

NEOXI Neural Network Resources

Content: Professionally selected extensive collection of neural network resources.
Audience: Communities of commerce, industry, academics, engineers, practitioners, and individuals interested in neural networks, machine learning, data mining, artificial intelligence, soft-computing, and numerous other fields directly or indirectly utilizing the neural network technology.

The Future Of Software

New research in artificial intelligence could lay the groundwork for computer systems that learn from their users and the world around them. Artificial intelligence, a field that has tantalized social scientists and high-tech researchers since the dawn of the computer industry, had lost its sex appeal by the start of the last decade.
Now a new generation of researchers hopes to rekindle interest in AI. Faster and cheaper computer processing power, memory, and storage, and the rise of statistical techniques for analyzing speech, handwriting, and the structure of written texts, are helping spur new developments, as is the willingness of today's practitioners to trade perfection for practical solutions to everyday problems.

Neural Network Programming in Java (by Jeff Heaton)

Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by IS programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics.

Neuroph - Lightweight Java neural network open source framework

Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the LGPL license, and it's FREE for you to use it.