Neural Network in R

Programming in any programming language recommended .

This course is an introduction to applying neural networks in real world problems using R-project software.

Introduction to Neural Networks

1. What are Neural Networks
2. What is current status in applying neural networks
3. Neural Networks vs regression models
4. Supervised and Unsupervised learning

Overview of packages available

1. nnet, neuralnet and others
2. Differences between packages and itls limitations
3. 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