Diploma in Neural Networks
Basics of Neural Networks.
Basics of Artificial Neural Networks:
Characteristics, history and terminology of neural networks, models, topology and learning concepts of neural networks.
Activation and Synaptic Dynamics:
learning basics and laws, dynamics and activation models, pattern recognition and stability concepts.
Feedforward Neural Networks:
Pattern association, pattern classification, weight determination, pattern mapping and storage analysis and the technique of backpropagation algorithm.
Feedback Neural Networks:
Basics of feedback neural networks, pattern storage network analysis, stochastic networks, boltman machine and analysis of autoassociative neural networks.
Competitive Learning Neural Networks:
feedback layer and feature mapping network analysis.
Architectures for Complex Pattern and Applications of ANN:
Associative networks, neural network applications and concepts of feedforward neural networks.
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