1. Introduction to Neural Networks And Their Applications
Submitted To
Dr. Md. Hasanuzzaman
Professor,Department of CSE,Dhaka University
Submitted By
Faria Hossain( )
Amirunnesa( )
Rebeka Sultana( )

2. Table of Contents
1. Introduction of Neural Networks
2. Application of Neural Networks
3. Theory of Neural Networks
4. A Neural Network Demo

3. 1.Introduction of Neural Networks
It is simulation of human brain
It is the most well known artificial intelligence techniques
We are using them: voice recognition system, reading hand writes, door rocks et al.
It is a called black box

4. It is a simulator for human brain
Neural Networks simulate human brain
Learning in Human Brain
Neurons
Connection Between Neurons
Neural Networks As Simulator For Human Brain
Processing Elements or Nodes
Weights

5. 2. Applications of Neural Networks
Prediction of Outcomes
Patterns Detection in Data
Classification

6. Business ANN Applications -1
Accounting
Finance
Human Resources
Management
Marketing
Operations

7. 3. Theory of Neural Networks
Neural Computing is a problem solving methodology that attempts to mimic how human brain functions
Artificial Neural Networks (ANN)
Machine Learning/Artificial Intelligence

8. The Biological Analogy
Neurons: brain cells
Nucleus (at the center)
Dendrites provide inputs
Axons send outputs
Synapses increase or decrease connection strength and cause excitation or inhibition of subsequent neurons

9. Artificial Neural Networks (ANN)
Biological Artificial
Soma <-> Node
Dendrites <-> Input
Axon <-> Output
Synapse <-> Weight
Three Interconnected Artificial Neurons

10. Neural Network Architecture
Feed forward Neural Network
: Multi Layer Perceptron, - Two, Three, sometimes Four or Five Layers, But normally 3 layers are common structure.

11. How a Network Learns
Step function evaluates the summation of input values
Calculating outputs
Measure the error (delta) between outputs and desired values
Update weights, reinforcing correct results
At any step in the process for a neuron, j, we get
Delta(Error) = Zj - Yj
where Z and Y are the desired and actual outputs, respectively

12. Training A Neural Networks
Neural Networks learn from data
Learning is finding the best weights values which represent the input and output relationship in Neural Networks
(ex: 4*X= 8)-> finding the value for X

13. training data set and test data set
Collect data and separate it into
Training set (50%), Testing set (50%)
Training set (60%), Testing set (40%)
Training set (70%), Testing set (30%)
Training set (80%), Testing set (20%)
Training set (90%), Testing set (10%)
Use training data set to build model
Use test data set to validate the trained network

14. Prediction with New Data
If the Neural Network's performance in test is good , it can be used to predict outcome of new unseen data
If the performance with test is not good, you should collect more data, add more input variables

15. How does Neural Network work for prediction?
Terms in Neural Networks

16. ANN Development Tools E-Miner Clementine NeuroSolutions NeuralWorks
Brainmaker
PathFinder
Trajan Neural Network Simulator
NeuroShell Easy
NeuroWare

17. Benefits of ANN Advantages:
Non-linear model leads to better performance
It works generally good when data size is small
It works generally good when there are noises in data
It works generally good when there are missing in data (incomplete data set)
Fast decision making
Diverse Applications:
Pattern recognition
Character, speech and visual recognition

18. Limitations of ANN Black box that is hardly understood by human
Lack of explanation capabilities
Training time can be excessive and tedious

19. 4. A Neural Networks Demo How do neural networks learn?
: trials and errors
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