1. III. Introduction to Neural Networks And Their Applications - Basics

2. Introduction to Neural Networks and Its Applications
I. Introduction of Neural Networks
II. Application of Neural Networks
III. Theory of Neural Networks
IV. An Example
. Weather Forcasting

3. I. Introduction of Neural Networks
Learning in Human Brain
Neurons
Connection Between Neurons
Neural Networks As Simulator For Human Brain
Processing Elements or Nodes
Weights

4. Main Applications of Neural Networks
Prediction of Outcomes
Patterns Detection in Data
Classification

5. Why use Neural Networks in Predict

6. II. Applications of Neural Networks
Computer Vision : Character recognition
HNC : Read amount in checks
NESTOR[Reilly et al , 1990]:Mortgage insurance decisions
DAS/LARS[Casselman and Acks,1990] : large diagnostic system
DECtalk[Sejnowski and Rosenberg, 1987] : Convert language to text
Manufacturing System Controller[Park & Kim, 1991]
: Ford motor Co..
Investment Decision Making System: Tong Yang Future & Options in Chicago

7. III. Theory of Neural Networks
Network Structure : Layers, Nodes and Weights
Hidden Layer
Input Layer
Output Layer

8. Training A Neural Networks
The Key to the success of Neural Networks use is collecting a lot of good data
Neural Networks learn from data
Learning is finding best weights values that represent the input and output relationship in Neural Networks

9. Terms in Neural Networks

10. Testing and Validating a Neural Networks
Testing data set : use another new data
Check the performance of trained Neural Networks with a testing data
If it’s performance of test is good , then check validity of Neural Networks with another new set of historical data

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

12. IV. A Neural Networks Demo
Intro to neural networks
Demo for stock market prediction