1 Introduction to Neural Networks And Their Applications

2 Table of Contents I. Introduction of Neural Networks II. Application of Neural Networks III. Theory of Neural Networks IV. A Neural Network Demo

3 What is neural networks ? =DG5- UyRBQD4&feature=rellist&playnext =1&list=PL4FA5D71B0BA92C1C =DG5- UyRBQD4&feature=rellist&playnext =1&list=PL4FA5D71B0BA92C1C

4 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 Networks

5 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 brain

6 II. Applications of Neural Networks Prediction of Outcomes Patterns Detection in Data Classification

7 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 -1

8 Business 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, …

9 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 Networks

10 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

11 Biological Artificial Soma Node Dendrites Input Axon Output Synapse Weight Artificial Neural Networks (ANN) Three Interconnected Artificial Neurons

12 Basic structure of Neural Networks Network Structure : Layers, Nodes and Weights Input Layer Hidden LayerOutput Layer

13 ANN Fundamentals

14 Processing Information by the Network Inputs Outputs Weights Summation Function Figure 15.5 ANN Fundamentals: how informatio is processed in ANN

15 Learning in NN(Neural Network) is finding the best numeric values (X), representing input (4) and output(8) relationship ( ex: 4 * X = 8 ) *Try with x= 1, x= 2, x=3, …… When x=4, it solve the problem. 1. Compute outputs 2. Compare outputs with desired targets 3. Adjust the weights and repeat the process

16 Neural Network Architecture There are several ANN architectures :feed forward, recurrent, Hopfield et al.

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

18 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) = Z j - Y j where Z and Y are the desired and actual outputs, respectively How a Network Learns

19 1. Initialize the weights 2. Read the input vector 3. Generate the output 4. 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 Backpropagation

20 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

21 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 set

22 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

23 Terms in Neural Networks How does Neural Network work for prediction?

24 Demo – How does Neural Network work for prediction?

25 ANN Development Tools E-Miner Clementine NeuroSolutions Statistica Neural Network Toolkit Braincel (Excel Add-in) NeuralWorks Brainmaker PathFinder Trajan Neural Network Simulator NeuroShell Easy SPSS Neural Connector NeuroWare

26 Why use Neural Networks in Prediction? - major benefits of Neural Networks

27 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

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

29 IV. A Neural Networks Demo How do neural networks learn? : trials and errors Str0Rdkxxo