1 introduction to neural networks and their applications
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Introduction to Neural Introduction to Neural Networks And Their Networks And Their
ApplicationsApplications
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Table of ContentsTable of Contents
I. Introduction of Neural NetworksII. Application of Neural NetworksIII. Theory of Neural NetworksIV. A Neural Network Demo
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What is neural networks ?What is neural networks ?
http://www.youtube.com/watch?v=DG5-UyRBQD4&feature=rellist&playnext=1&list=PL4FA5D71B0BA92C1C
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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
I. Introduction of Neural I. Introduction of Neural NetworksNetworks
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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
It is a simulator for human brainIt is a simulator for human brain
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II. Applications of Neural II. Applications of Neural NetworksNetworks Prediction of Outcomes
Patterns Detection in Data
Classification
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Accounting Identify tax fraud Enhance auditing by finding irregularities
Finance Signatures and bank note verifications Foreign exchange rate forecasting Bankruptcy prediction Customer credit scoring Credit card approval and fraud detection* Stock and commodity selection and trading Forecasting economic turning points Pricing initial public offerings* Loan approvals
Business ANN Applications -1Business ANN Applications -1
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Business ANN Applications -2Business ANN Applications -2
Human Resources Predicting employees’ performance and behavior Determining personnel resource requirements
Management Corporate merger prediction Country risk rating
Marketing Consumer spending pattern classification Sales forecasts Targeted marketing, …
Operations Vehicle routing Production/job scheduling, …
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Neural Computing is a problem solving methodology that attempts to mimic how human brain functions
Artificial Neural Networks (ANN)
Machine Learning/Artificial Intelligence
III. Theory of Neural III. Theory of Neural NetworksNetworks
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The Biological AnalogyThe 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
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Biological ArtificialSoma <-> NodeDendrites <-> InputAxon <-> OutputSynapse <-> Weight
Artificial Neural Networks (ANN)Artificial Neural Networks (ANN)
Three Interconnected Artificial Neurons
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Basic structure of Neural NetworksBasic structure of Neural Networks
Network Structure : Layers, Nodes and Weights
Input Layer Hidden Layer Output Layer
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ANN FundamentalsANN Fundamentals
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Processing Information by the Network Inputs Outputs Weights Summation Function
Figure 15.5
ANN Fundamentals: how ANN Fundamentals: how informatio is processed in ANNinformatio is processed in ANN
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Learning in NN(Neural Network) Learning in NN(Neural Network) is finding the best numeric is finding the best numeric values (X), representing input values (X), representing input (4) and output(8) relationship (4) and output(8) relationship ( ex: 4 * X = 8 )( ex: 4 * X = 8 )*Try with x= 1, x= 2, x=3, …… When x=4, it solve the problem.*Try with x= 1, x= 2, x=3, …… When x=4, it solve the problem.
1. Compute outputs2. Compare outputs
with desired targets
3. Adjust the weights and repeat the process
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Neural Network ArchitectureNeural Network Architecture
There are several ANN architectures :feed forward, recurrent, Hopfield et al.
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Neural Network ArchitectureNeural Network Architecture
Feed forward Neural Network: Multi Layer Perceptron, - Two, Three, sometimes Four or Five Layers, But normally 3 layers are common structure.
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Step function evaluates the summation of input values
Calculating outputs Measure the error (delta) between outputs
and desired values Update weights, reinforcing correct resultsAt any step in the process for a neuron, j, we
getDelta(Error) = Zj - Yj
where Z and Y are the desired and actual outputs, respectively
How a Network LearnsHow a Network Learns
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1. Initialize the weights2. Read the input
vector3. Generate the output4. Compute the error
Error = Output – Desired output
5. Change the weights
Drawbacks: A large network can take a very long time to
train May not converge
BackpropagationBackpropagation
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Training A Neural NetworksTraining 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
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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
training data set and test data training data set and test data setset
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Prediction with New DataPrediction 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
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Terms in Neural Networks
How does Neural Network work for prediction?
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Demo – How does Neural Network work for prediction?
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ANN Development ToolsANN Development Tools
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Why use Neural Networks in Why use Neural Networks in Prediction? - major benefits of Prediction? - major benefits of
Neural NetworksNeural Networks
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Benefits of ANNBenefits of ANN
Advantages:Non-linear model leads to better performanceIt works generally good when data size is smallIt works generally good when there are noises in dataIt works generally good when there are missing in data (incomplete data set)Fast decision making
Diverse Applications:Pattern recognitionCharacter, speech and visual recognition
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Limitations of ANNLimitations of ANN
Black box that is hardly understood by human
Lack of explanation capabilities Training time can be excessive
and tedious
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IV. A Neural Networks DemoIV. A Neural Networks Demo
How do neural networks learn? : trials and errorshttp://www.youtube.com/watch?v=0Str0Rdkxxo