NeuronDotNet - User Manual
- Introduction to NeuronDotNet
- NeuronDotNet Objectives
- Getting Started
- NeuronDotNet Basics
- Neural Classifier Sample
- Digit Recognition Sample
Introduction to NeuronDotNet
NeuronDotNet is an open source platform to develop and run AI applications based on 'Back-Propagation Artificial Neural Networks'. It is written in C# and is compatible with the .NET platform. This tool exploits the potentialities of object oriented design and modular programming. The objective of the project is to support the development of real world AI applications and also to support experimental research in the field by providing a centralized framework for creation, training and usage of different types of artificial neural networks.
'Artificial Neural Network' is an information processing paradigm, which attempts to simulate the functionality of human brain. Neural networks have a remarkable property of deriving meaningful information from complicated or imprecise data, and thus can be used to extract patterns and detect trends that are too complex to be noticed by other computing techniques.
Neural networks are extensively used in next generation computing models and pattern recognition problems like optical character recognition, face recognition, voice recognition, speech recognition, fingerprint recognition and also in financial and economic modelling, enterprise applications for sales forecasting, industrial process control, data validation, customer research etc..
NeuronDotNet is a tool which simplifies development of such AI Applications by providing a neural engine for designing complex neural networks.