1. ARTIFICIAL NEURAL NETWORKS

2. CONTENTS INTRODUCTION AND HISTORY OF NEURAL NETWORKS
ANOLOGY OF THE BRAIN
BIOLOGICAL NEUTRON
ARTIFICIAL NEURON
DESIGN
LAYERS
COMMUNICATION AND TYPES OF CONNECTIONS
LEARNING
OFF-LINE AND ON-LINES
LEARNING LAWS
WHERE ARE NEURAL NETWORKS BEGIN USED
CONCLUSION
REFERENCES

3. INTRODUCTION
HISTORY

4. ANOLOGY OF BRAIN

5. BIOLOGICAL NEURON Simplified Biological Neuron
Biological Neuron with Cell Body

6. Simplified Biological Neuron

7. Biological Neuron with cell body

8. ARTIFICIAL NEURON

9. DESIGN
DIAGRAM

10. COMPLEX RELATIONSHIPS
FLOW
INPUT LAYER
MIDDLE LAYER
OUTPUT LAYER
NEURONS
CONNECTION PATHS

11. LAYERS

12. Communications and its types
Inter-layer Connections
Intra-layer Connections

13. Inter-layer Connections
Fully Connected
Partially Connected
Feed Forward
Bi-directional
Hierarchical
Resonance

14. Intra-layer Connections
Recurrent
On-center/off surround

15. LEARNING
Unsupervised Learning
Reinforcement learning
Back propagation

16. Off-line and On-line
Off-Line
On-Line

17. LEARNING LAWS Hebb’s Rule Hopfield Rule The Delta Rule
Kohonen’s Learning Rule

18. WHERE ARE NEURAL NETWORKS BEING USED?
Where they are used?
APPLICATIONS

19. Where they are used? Stock Market Forecast
Nestor – Financial Risk Assessment
Net-talks
Signatures
Speech
Bomb Detector - Snoope

20. APPLICATIONS Prediction Classification Data Association
Data Conceptualization
Data Filtering

21. CONCLUSION

22. REFERENCES http://www.ccs.neu.edu/home/cloder/

23. Questions

24. Thank you
Sireesha…..

25. It’s an
MAHESH
Presentation