Explorations in Neural Networks

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