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• 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