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1. Introduction to Neural Networks
   What are Neural Networks
   What is current status in applying neural networks
   Neural Networks vs regression models
   Supervised and Unsupervised learning
   Overview of packages available
   nnet, neuralnet and others
   Differences between packages and itls limitations
   Visualizing neural networks
   Applying Neural Networks
   Concept of neurons and neural networks
   A simplified model of the brain
   Opportunities neuron
   XOR problem and the nature of the distribution of values
   The polymorphic nature of the sigmoidal
   Other functions activated
   Construction of neural networks
   Concept of neurons connect
   Neural network as nodes
   Building a network
   Neurons
   Layers
   Scales
   Input and output data
   Range 0 to 1
   Normalization
   Learning Neural Networks
   Backward Propagation
   Steps propagation
   Network training algorithms
   range of application
   Estimation
   Problems with the possibility of approximation by
   Examples
   OCR and image pattern recognition
   Other applications
   Implementing a neural network modeling job predicting stock prices of listed