Neural networks

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Neural networks

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

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