Neural Networks

Steven Le

Overview

• Introduction• Architectures• Learning Techniques• Advantages• Applications

Introduction

• A Neural Network is data processing model which is composed of a large number of processing elements which individually handle one piece of a larger problem

• Two main types of neural networks

Biological Neural Networks

• The human brain is a neural network• Nervous system is composed of neurons• Signals travel into the neuron via dendrites• Signals are sent

out via the axon

• Signals coming into the dendrite can be either exhibitive or inhibitive

• Synapses may add resistance before adding• A Threshold determines if the neuron is

excited enough to send a signal out through the axon

Artificial Neural Networks

• Try to simulate how biological neural networks process information

• Acquires knowledge through learning• Knowledge is stored within inter-neuron

connection strengths known as synaptic weights.

Model of an Artificial Neuron

• Synaptic weights are multiplied with an input to give the weighted input

• Activation function computes the values of every input and if they exceed the threshold, the neuron will fire

• Output, like the biological version, can either be -1 or 1 (alternatively 0 or 1)

Architectures

Feed-Forward Networks

• Signals only travel in one direction• Output of a layer doesn’t affect the same layer

Feedback Networks

• Signals travel any direction and can loop• Node states are always changing until an

equilibrium is reached• Remains at rest

until new input isintroduced or newequilibrium is needed

Learning Techniques

• Before they are used, neural networks go through a learning phase in which they acquire knowledge

Supervised Learning

• Incorporates an external teacher so that each output unit is told what its desired response to input signals should be

• The aim is to determine a set of weights which minimizes the error between actual and desired outputs

Unsupervised Learning

• Uses no external teacher and is based upon only local information.

• Also known as Self-Organization, the output unit is trained to respond to clusters of pattern within the input

• No pre-set categories

Advantages

• Parallelism: Neurons act independently• Adaptive learning• Self-organization• Fault tolerance• Interacting with noisy data

Applications

• Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting

• Some applications include: targeted marketing, voice recognition, financial forecasting, data validation, and credit evaluation

Examples

• A company has a database of 1million potential customers. 20,000 (2%) response is the goal

• Contact 100,000. Use this subset to train the neural network

• Present the other 900,000 to the neural network which will classify 2% of them as buyers

Optical Character Recognition

End

References

• Null, Linda. Computer Organization and Architecture.• http://ulcar.uml.edu/~iag/CS/Intro-to-ANN.html• http://www.nd.com/welcome/whatisnn.htm• http://www.learnartificialneuralnetworks.com/• http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/

cs11/report.html