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Neural Networks
Steven Le
Overview
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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
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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 is
introduced or new
equilibrium 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
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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
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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/r
eport.html