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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 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 • • • • • 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/r eport.html