neural network ppt presentation
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NEURAL NETWORK
BY…
SIDDHARTH PATEL
CLASS: IT-B (SEM: V)
ENR.NO: 100530116032
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CONTENTS :
IntroductionArchitectureHuman and Artificial NeuronesApplicationsAdvantagesDisadvantagesNeural network in futureConclusion
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1. INTRODUCTION .
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1.1 WHAT IS A NEURAL NETWORK?
NN is an information processing paradigm . The key element of this paradigm is the
novel structure.
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1.2 WHY USE NEURAL NETWORKS?
Adaptive learning. Self-Organisation. Real Time Operation.
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2. ARCHITECTURE .
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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 .
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2.2 FEEDBACK (AUTO ASSOCIATIVE) NETWORKS Signals travelling in
both directions. It is dynamic. Their 'state' is
changing continuously.
It is very powerful.
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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.
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3.HUMAN AND ARTIFICIAL NEURONES
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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.
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Components of a neuron
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The synapse
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4.APPLICATIONS
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4.1 NEURAL NETWORKS IN BUSINESS
Sales forecasting Industrial process control Customer research Data validation Risk management Target marketing
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4.2 NEURAL NETWORKS IN MEDICINE
cardiograms CAT scans ultrasonic scans, etc…
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4.3 NEURAL NETWORKS IN BUSINESS
Marketing Credit Evaluation Stock Market
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OTHER APPLICATIONS
Character Recognition Image Compression Food Processing Signature Analysis Monitoring
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5.ADVANTAGES: Adapt to unknown situation. Autonomous learning & generalization. Robustness: fault tolerance due to network
redundancy. Noise tolerance Ease of maintenance
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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.
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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.
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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.
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THANK YOU…