JNTUH B.Tech 4th year (4-2) Artificial Neural Networks gives you detail information of Artificial Neural Networks (Elective – III) R13 syllabus It will be help full to understand you complete curriculum of the year.<\/p>\n
UNIT- I<\/strong><\/p>\n Introduction – what is a neural network? Human Brain, Models of a Neuron, Neural networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks Learning Process \u2014 Error Correction learning, Memory based learning, Hebbian learning, Competitive, Boltzmann learning, Credit Assignment Problem, Memory, Adaption, Statistical nature of the learning process.<\/p>\n UNIT-II<\/strong><\/p>\n Back Propagation: back propagation and differentiation, Hessian matrix, Generalization, Cross validation, Network pruning Techniques, Virtues and limitations of back propagation learning, Accelerated convergence, supervised learning.<\/p>\n UNIT- III<\/strong><\/p>\n Single Layer Perceptions: Adaptive filtering problem, Unconstrained Organization Techniques, Linear least square filters, least mean square algorithm, learning curves, Learning rate annealing techniques, perceptron \u2014 convergence theorem, Relation between perceptron and Bayes classifier for a Gaussian Environment Multilayer Perceptron \u2014 Back propagation algorithm XQR problem, Heuristics, Output representation and decision rule, Computer experiment, feature detection.<\/p>\n