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Explorations in Neural Networks
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Tianhui Cai
Period 3
Definition
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Mathematical model based on biological
neural networks
Interconnected group of artificial neurons
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Highly connected
Each unit is simple, but the system is complex
Neurons have output value, which is
determined by the outputs of other nodes that
feed into it
Nodes are connected by directed links with
weights
Adaptive system – changes structure
Can model complicated functions
Neural Networks
Feedforward networks
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Information moves in one direction – forward
Input -> hidden -> output
Single layer perceptron
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Consists of a single layer of output nodes
Inputs are fed directly to output with weights
Sum of weights * inputs is calculated for a node
Neuron 'fires' based on activation function
Limited functionality.
Multi-layer perceptron
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Multiple layers
Learns through back-propagation
Applications
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Pattern recognition
Classification
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Handwritten digit classification
Useful for reading zip codes
Can deal with noisy samples
Current status
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Can make a neural network
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Feedforward
Multiple layers
Neural networks can take input and spit out
output correctly
Backpropagation
Binary data – AND, OR, XOR
Future
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Gather input data for handwriting samples
Alter program to process handwriting
samples / images
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more inputs
more outputs
Test variations
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Number of hidden layers
Number of hidden neurons
Specific connections