1. Explorations in Neural Networks Tianhui Cai Period 3

2. Definition Mathematical model based on biological neural networks Interconnected group of artificial neurons  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

3. Neural Networks

4. Feedforward networks Information moves in one direction – forward Input -> hidden -> output Single layer perceptron  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  Multiple layers  Learns through back-propagation

5. Applications Pattern recognition Classification  Handwritten digit classification  Useful for reading zip codes  Can deal with noisy samples

6. Current status Can make a neural network  Feedforward  Multiple layers Neural networks can take input and spit out output correctly Backpropagation Binary data – AND, OR, XOR

7. Future Gather input data for handwriting samples Alter program to process handwriting samples / images  more inputs  more outputs Test variations  Number of hidden layers  Number of hidden neurons  Specific connections