Machine Learning Neural Networks Example
Dr. Tony Martinez and Dr. Dan Ventura
Neural networking is the science of creating computational solutions modeled after the brain. Like the human brain, neural networks are trainable-once they are taught to solve one complex problem, they can apply their skills to a new set of problems without having to start the learning process from scratch. Students working with Dr. Dan Ventura and Dr. Tony Martinez in the Neural Networks and Machine Learning Laboratory are taking these theories and applying them to complex problems.
The Lab seeks models which combine the best aspects of neural network mechanisms and symbolic artificial intelligence machine learning paradigms. Neural networks and machine learning algorithms represent a dramatic departure from conventional programming techniques. Rather than explicitly build a program to solve a problem, examples, called "training sets, " of a type of problem are given, which the neural network "learns" how to solve. The network can then be presented with new examples on which it was not trained, known as "test sets, " and it will use the skills it gained from the training
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