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Neural network
- 1. NEURAL NETWORK Introduction to neural networks 1
- 2. Introduction to neural networks 2
- 3. Introduction to neural networks 3
- 4. Introduction to neural networks 4
- 5. Introduction to neural networks 5
- 6. Introduction to neural networks 6
- 7. Introduction to neural networks 7
- 8. Introduction to neural networks 8
- 9. Introduction to neural networks 9
- 10. Introduction to neural networks 10
- 11. Introduction to neural networks 11
- 12. Introduction to neural networks 12
- 13. Introduction to neural networks 13
- 14. Can computers think like humans? Introduction to neural networks 14
- 15. How computers work? Introduction to neural networks 15 3 1 2 Input B A Output
- 16. Introduction to neural networks 16 How human brain works?
- 17. Introduction to neural networks 17 Neural Networks • Is an information processing paradigm • Composed of simple units / nodes / neurons • Neurons are connected to each other creating network
- 18. Introduction to neural networks 18 node node node node node node node node
- 19. Introduction to neural networks 19 node node node node node node node node
- 20. Introduction to neural networks 20 node node node node node node node node
- 21. Introduction to neural networks 21 node node node node node node node node
- 22. Introduction to neural networks 22 node Input Nodes Output Nodes node node node node node node node node node node node node node
- 23. Introduction to neural networks 23 node Input Nodes Output Nodes node node node node node node Hidden nodes
- 24. How neural network works? •Each node has specific feature or job •Nodes take input analyze them •Transfer the information to other nodes as input Introduction to neural networks 24
- 25. Introduction to neural networks 25
- 26. Introduction to neural networks 26
- 27. Introduction to neural networks 27
- 28. Introduction to neural networks 28
- 29. Introduction to neural networks 29
- 30. Introduction to neural networks 30
- 31. Introduction to neural networks 31
- 32. Introduction to neural networks 32
- 33. Introduction to neural networks 33
- 34. Backpropagation: •Most common method for teaching a neural network •Uses iterative way Introduction to neural networks 34
- 35. Introduction to neural networks 35 node Input Nodes Output Nodes node node node node node node node node node node node node node Random weights
- 36. Introduction to neural networks 36 node Input Nodes Desired Output node node node node node node node node node node node node node
- 37. Calculated Output node node node Desired Output node node node ? Calculated Output node node node Desired Output
- 40. Introduction to neural networks 40Introduction to neural networks 40 node Input Nodes node node node node node node node node node node Modified weights ModifiedValue
- 41. Introduction to neural networks 41 node Input Nodes node node node node node node ModifiedValue Modified Weight
- 42. Introduction to neural networks 42Introduction to neural networks 42 node Input Nodes Calculated Output node node node node node node node node node node node node node
- 43. Pros: •Can be taught •No need of complex algorithms •Real time computation •Parallel processing •Helps in pattern recognition Introduction to neural networks 43
- 44. Cons: •Requirement of lots of data while teaching •Slow rate of learning •Complexity increases with no. of nodes •Difficult to model Introduction to neural networks 44
- 45. Applications: •Recognition •Forecasting •Artificial intelligence •Researches •Interpretation •Generation Introduction to neural networks 45
- 46. Conclusion Introduction to neural networks 46
Editor's Notes
- Introduction