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NEURAL NETWORKS


This is a course in practical Neural Networks. Though advanced Caltech CS courses provide a theoretical and highly rigorous treatment of machine learning, Practical Neural Networks would be a brief and instructive overview designed for casual programmers less interested in the nuances of the theory who simply want to include neural networks in their programming work. We will go over single and multi-layer, fully-connected perceptrons including simplified error analysis and backpropagation, starting at the most fundamental level. The course will also teach students about industrial and academic applications of neural networks and how to recognize tasks that neural networks are suitable for. Only a rudimentary knowledge of programming is required for this course.




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