Diploma in Neural Networks

0 STUDENTS ENROLLED


     

    Syllabus

    Introduction:

    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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