It's a neural network ppt presentation

1. NEURAL NETWORK
BY…
SIDDHARTH PATEL
CLASS: IT-B (SEM: V)
ENR.NO: 100530116032

2. CONTENTS :
 Introduction
 Architecture
 Human and Artificial Neurones
 Applications
 Advantages
 Disadvantages
 Neural network in future
 Conclusion

3. 1. INTRODUCTION .

4. 1.1 WHAT IS A NEURAL NETWORK?
 NN is an information processing paradigm .
 The key element of this paradigm is the novel
structure.

5. 1.2 WHY USE NEURAL NETWORKS?
 Adaptive learning.
 Self-Organisation.
 Real Time Operation.

6. 2. ARCHITECTURE .

7. 2.1 FEED-FORWARD (ASSOCIATIVE)
NETWORKS
 Allow signals to travel
one way only; from
input to output.
 There is no feedback.
 It tend to be straight
forward networks .

8. 2.2 FEEDBACK (AUTO ASSOCIATIVE)
NETWORKS
 Signals travelling in
both directions.
 It is dynamic.
 Their 'state' is
changing continuously.
 It is very powerful.

9. 2.3 NETWORK LAYERS.
I. Input: represents the
raw information.
II. Hidden: determined
by the activities of the
input units .
III. Output: depends on
the activity of the
hidden units.

10. 3.HUMAN AND ARTIFICIAL
NEURONES

11. 3.1 HOW THE HUMAN BRAIN LEARNS?
 Neuron collects signals from others through a host
called dendrites.
 Neuron sends out spikes of electrical activity
through a long, thin stand known as an axon.
 A synapse converts the activity from the axon into
electrical effects that excite activity from the axon in
the connected neurones.

12. Components of a
neuron

13. The synapse

14. 4.APPLICATIONS

15. 4.1 NEURAL NETWORKS IN BUSINESS
 Sales forecasting
 Industrial process control
 Customer research
 Data validation
 Risk management
 Target marketing

16. 4.2 NEURAL NETWORKS IN MEDICINE
 cardiograms
 CAT scans
 ultrasonic scans, etc…

17. 4.3 NEURAL NETWORKS IN BUSINESS
 Marketing
 Credit Evaluation
 Stock Market

18.  OTHER APPLICATIONS
 Character Recognition
 Image Compression
 Food Processing
 Signature Analysis
 Monitoring

19. 5.ADVANTAGES:
 Adapt to unknown situation.
 Autonomous learning & generalization.
 Robustness: fault tolerance due to network
redundancy.
 Noise tolerance
 Ease of maintenance

20. 6.DISADVANTAGES:
 No exact.
 Large complexity of the network structure.
 NN needs training to operate.
 Requires high processing time for large NN.
 NN sometimes become unstable.

21. 7.NEURAL NETWORK IN FUTURE
 Robots that can see, feel, and predict the world
around them.
 Composition of music.
 Handwritten documents to be automatically
transformed into formatted word processing
documents.
 Self-diagnosis of medical problems using neural
networks.

22. 8.CONCLUSION:
 Their ability to learn by example makes them very
flexible and powerful. There is no need to devise an
algorithm to perform a specific task. There is no
need to understand the internal mechanisms of that
task. They are also very well suited for real time
systems.

23. THANK YOU…