A presentation on a basic introduction to Neural Networks

1. NEURAL NETWORKS
Guided By
Prof. Sohan Pandey
Submitted By-
Yash Raghava
1601341077
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SILICON INSTITUTE OF TECHNOLOGY, SAMBALPUR

2. INTRODUCTION
AN ARTIFICIAL NEURAL NETWORK (ANN) IS AN INFORMATION
PROCESSING PARADIGM THAT IS INSPIRED BY THE WAY
BIOLOGICAL NERVOUS SYSTEMS WORKS, SUCH AS THE BRAIN,
PROCESS INFORMATION.

3. BIOloGICAL NERVOUS SYSTEM

4. BIoLOGICAL NERVOUS SYSTEM

5. BIoLOGICAL NERVOUS SYSTEM

6. Artificial neurons

7. Artificial neurons

8. Artificial neurons

9. Artificial neurons

10. Example of an Artificial neuron

11. NEURAL NETWORKS VS
CONVENTIONAL NETWORKS1. LEARNING APPROACH VS ALGORITHMIC APPROACH
2. NOT PROGRAMMED FOR SPECIFIC TASKS VS PROGRAMMED
FOR SPECIFIC TASKS
3. USED FOR DECISION MAKING VS WORK ON PREDEFINED SET
OF INSTRUCTIONS
4. OPERATION IS UNPREDICTABLE VS OPERATION IS
PREDICTABLE

12. TYPES OF LEARNING

13. TYPES OF LEARNING
SUPERVISED LEARNING AND UNSUPERVISED LEARNING

14. TYPES OF LEARNING
REINFORCEMENT LEARNING

15. TYPES OF LEARNING
REINFORCEMENT LEARNING

16. TYPES OF LEARNING
BACKTRACKING

17. APPLICATIONS
PREDICTION
CLASSIFICATION
DATA ASSOCIATION
DATA CONCEPTUALIZATION
DATA FILTERING

18. CLASSIFICATION

19. Image recognition

20. Self driving cars

21. Language translator

22. Virtual assistant

23. gaming

24. musenet

25. Other applications
• STOCK MARKET FORECASTING
• WEATHER FORECASTING
• CUSTOMER RESEARCH
• TARGET MARKETING
• FINANCIAL RISK MANAGEMENT
• IN MEDICINES AND MODELLING THE CARDIOVASCULAR SYSTEMS
• CREDIT EVALUATION AND LOAN SCORING

26. conclusion

27. THANK YOU