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Introduction: Concepts of neural networks, Characteristics of Neural Networks, Historical Perspective, and Applications of Neural Networks.
Fundamentals of Neural Networks: The biological prototype, Neuron concept, Single layer Neural Networks, Multi-Layer Neural Networks, terminology, Notation and representation of Neural Networks, Training of Artificial Neural Networks.Representation of perceptron and issues, perceptron learning and training, Classification, linear Separability.
Hopfield nets: Structure, training, and applications, Stability
Back propagation: Concept, Applications, and Back Propagation Training Algorithms. Counter Propagation Networks: Kohonan Network, Grossberg Layer & Training, applications of counter propagation, Image classification.
Bi-directional Associative Memories: Structure, retrieving a stored association, encoding associations, memory capacity.ART: ART architecture, ART classification operation, ART implementation, and characteristics of ART.Image Compression Using ART
Optical Neural Networks: Vector Matrix Multipliers, Hop field net using Electro optical matrix multipliers, Holographic correlator, Optical Hopfield net using Volume Holograms.The Cognitrons and Neocognitrons: Their structure and training.Genetic Algorithms: Elements, a simple genetic algorithm, working of genetic algorithms evolving neural networks.
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