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Neural networks

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Transcript

  • 1. Neural Networks Artificial Intelligence and
  • 2. CONTENTS
    • INTRODUCTION
    • HUMAN AND ARTIFICIAL NEURONS
    • AN ENGINEERING APPROACH
    • ARCHITECTURE OF NEURAL NETWORKS
    • THE LEARNING PROCESS
    • APPLICATIONS
    • CONCLUSION
  • 3. INTRODUCTION
    • What is a Neural Network?
    • It is an information processing paradigm that is inspired by the way biological nervous system, such as brain, process information.
    • Historical Background.
    • Why we use Neural Network?
    • Adaptive learning, Self-Organisation
    • Neural Networks Vs Conventional Computers.
  • 4. Human and Artificial Neurons
    • How the Human Brain Learns?
    Components of a neurons
  • 5.
    • From Human Neurons to Artificial Neurons
  • 6. An Engineering Approach
    • A simple Neuron
  • 7. Firing Rules:
    • Example:
    After Applying Firing X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0/1 0/1 0/1 1 0/1 1 X1: 0 0 0 0 1 1 1 1 X2: 0 0 1 1 0 0 1 1 X3: 0 1 0 1 0 1 0 1 OUT: 0 0 0 0/1 0/1 1 1 1
  • 8. Architecture Of Neural Networks
    • Feed-Forward Networks
    • Feed-Back Networks
    • Network Layers
    An example of a simple feed forward network An example of a complicated network
  • 9. The Learning Process
    • Association Mapping
    • Auto-association
    • Hetero-association
    • Nearest neighbour recall
    • Interpolative recall
    • Regularity Detection
    • Types of NN based on Weights
    • Fixed Networks
    • Adaptive Networks
  • 10.
    • Transfer Function
    • Linear
    • Threshold
    • Sigmoid
    • Back-Propagation Algorithm
    • Types Of Learning
    • Supervised Learning
    • Unsupervised Learning
  • 11. Applications
    • Sales Forecasting
    • Industrial Process Control
    • Customer Search
    • Data Validation
    • Risk Management
    • Target Marketing
  • 12.
    • Neural Networks in Medicine:
    • Modelling and diagnosing Cardiovascular system
    • Electronic Noses
    • Instant Physician
    • Neural Networks in Business:
    • Marketing
    • Credit Evaluation
    • Forecasting the Demand and Sales
  • 13. Conclusion
    • The computing world has a lot to gain from neural networks.
    • They are also well suited for real time systems.
    • NN also contribute to other areas of research such as neurology and pschology.
  • 14. THANK YOU !!!
  • 15. QUERIES ???

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