Artificial Intelligence
Artificial Intelligence :
The use of programs to enable machines to perform tasks which humans perform using their intelligence. Early AI avoided human pychological models, but this orientation has been altered by the development of connectionism, which is based on theories of how the brain works. In connectionism, complex functions, including learning, involve the transmission of information along pathways formed among large arrays of simple elements
From: www.filosofia.net/materiales/rec/glosaen.htm
Feed forward neural network - Java
This is a java implementation of some types of feed forward neural networks. You can simulate multilayer perceptron nets, generalized feed forward nets, modular feed forward nets using this package. You can use various back propagation methods. The source code is available and free.
What can you do with it?
You can:
- Build multilayer perceptron, generalized feed forward, modular feed forward nets;
- Train your net using batch, mini batch and incremental training;
- Create pattern sets out of semicolon separated files, train the net using these patterns,
- Spare some of the patterns for cross validation and testing, calculate error terms out of these patterns;
- Load and save network configuration and weights;
- Use logistic, tanh and linear activation functions;
- Use momentum;
- Determine the flatness of the activation functions;
- Use different momentum rate, flatness, learning rates for each neuron.
What can you do with it?
You can:
- Build multilayer perceptron, generalized feed forward, modular feed forward nets;
- Train your net using batch, mini batch and incremental training;
- Create pattern sets out of semicolon separated files, train the net using these patterns,
- Spare some of the patterns for cross validation and testing, calculate error terms out of these patterns;
- Load and save network configuration and weights;
- Use logistic, tanh and linear activation functions;
- Use momentum;
- Determine the flatness of the activation functions;
- Use different momentum rate, flatness, learning rates for each neuron.
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