Neural Networks Products
-----
 /
Neural Networks
*Who's It For?
<Features
*Examples
*Table of Contents
*Resources
*Q&A
*Download Product
Manual
*Buy Online
*Documentation
*Training
*For More Information
*Ask about this page
*Print this page
*Give us feedback
*Sign up for the Wolfram Insider

Features

Easy to Use and Learn
  • Small number of functions constructed so that only the minimum amount of information has to be specified by the user
  • Well-organized palettes with command templates, options, and links to online documentation
  • Intelligent initialization algorithms to begin the training with good performance and speed
  • Extensive documentation including an introduction to neural network theory as well as highly illustrative application examples


Support for Proven Neural Network Paradigms
  • Support for most of the commonly used neural network structures including radial basis function, feedforward, dynamic, Hopfield, perceptron, vector quantization, unsupervised, and Kohonen networks
  • Support for advanced training algorithms including Levenberg-Marquardt, Gauss-Newton, and steepest descent as well as for traditional algorithms including backpropagation with and without momentum
  • Support for typical neural network applications including function approximation, classification, dynamic systems modeling, time series, auto-associative memory, clustering, and self-organizing maps


Powerful Modeling Environment
  • Visualization tools for viewing network models, the training process, and network performance
  • Special network object to identify the type of network and list its parameters and properties
  • Special training record to keep intermediate information from the learning process
  • Functions equipped with a large number of advanced options to modify and control the training algorithms
  • Support for neural networks with any number of hidden layers and any number of neurons (hidden neurons) in each layer
  • Access to all of the capabilities of Mathematica to prototype new algorithms or to perform further manipulations on neural network structures


Fast and Reliable
  • Optimization of expressions before numerical evaluation to minimize the number of operations and to reduce computational load
  • Compile command to send compiled code directly to Mathematica to increase computational speed
  • Special performance-evaluation functions included to validate and illustrate the quality of a mapping
Any questions about topics on this page? Click here to get an individual response.