Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Neural Networks - Temple Fox MIS
Document related concepts
Neuroesthetics wikipedia , lookup
Neuroethology wikipedia , lookup
Cortical cooling wikipedia , lookup
Neuroeconomics wikipedia , lookup
Artificial general intelligence wikipedia , lookup
Neurocomputational speech processing wikipedia , lookup
Neural coding wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Neural oscillation wikipedia , lookup
Synaptic gating wikipedia , lookup
Neural modeling fields wikipedia , lookup
Optogenetics wikipedia , lookup
Biological neuron model wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Gene expression programming wikipedia , lookup
History of artificial intelligence wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Central pattern generator wikipedia , lookup
Artificial intelligence wikipedia , lookup
Pattern recognition wikipedia , lookup
Metastability in the brain wikipedia , lookup
Nervous system network models wikipedia , lookup
Development of the nervous system wikipedia , lookup
Neural engineering wikipedia , lookup
Catastrophic interference wikipedia , lookup
Artificial neural network wikipedia , lookup
Convolutional neural network wikipedia , lookup
Transcript
Neural Networks Week 5 Applications Predict the taste of Coors beer as a function of its chemical composition What are Artificial Neural Networks? Artificial Intelligence (AI) Technique Artificial Neural Networks (ANN) are biologically inspired and attempt to build computer models that operate like a human brain These networks can “learn” from the data and recognize patterns Basic Concepts of Neural Networks Biological and artificial neural networks Neurons Cells (processing elements) of a biological or artificial neural network Nucleus The central processing portion of a neuron Dendrite The part of a biological neuron that provides inputs to the cell Basic Concepts of Neural Networks Biological and artificial neural networks Axon An outgoing connection (i.e., terminal) from a biological neuron Synapse The connection (where the weights are) between processing elements in a neural network Basic Concepts of Neural Networks Basic Concepts of Neural Networks Relationship Between Biological and Artificial Neural Networks Soma – Node Dendrites – Input Axon – Output ANNs typically have much fewer neurons than humans Basic Concepts of Neural Networks Network structure (three layers) Input Intermediate (hidden layer) Output Basic Concepts of Neural Networks Basic Concepts of Neural Networks Transformation function (activation function) maps the summation (combination) function onto a narrower range ( 0 to 1 or -1 to 1) to determine whether or not an output is produced (neuron fires) The transformation occurs before the output reaches the next level in the network Sigmoid (logical activation) function: an S-shaped transfer function in the range of zero to one –exp(x)/(1exp(x)) Threshold value is sometimes used instead of a transformation function A hurdle value for the output of a neuron to trigger the next level of neurons. If an output value is smaller than the threshold value, it will not be passed to the next level of neurons Neural Network Prediction Formula hidden unit prediction estimate bias estimate weight estimate 1 tanh -5 0 -1 5 activation function ... Neural Network Binary Prediction Formula 5 logit link function 1 tanh -50 0 -5 -1 15 ... Learning in ANN Learning algorithm The training procedure used by an artificial neural network Supervised learning A method of training artificial neural networks in which sample cases are shown to the network as input and the weights are adjusted to minimize the error in its outputs Learning in ANN Learning in ANN How a network learns Backpropagation The best-known supervised learning algorithm in neural computing. Learning is done by comparing computed outputs to desired outputs of historical cases Learning in ANN How a network learns Procedure for a learning algorithm 1. Initialize weights with random values and set other parameters 2. Read in the input vector and the desired output 3. Compute the actual output via the calculations, working forward through the layers 4. Compute the error 5. Change the weights by working backward from the output layer through the hidden layers Learning in ANN Error calculation and weights At each hidden node and target node: compute: Linear combination function: C = w0 + w1x1 +…+ wnxn Logistic activation function: L = exp(C)/(1+exp(C) At the target node compute Bernoulli error function: sum errors over all observations, where the error is -2 ln (L) if there is a response, or -2 ln (1 – L) if there is no response In the first iteration, random weights are used In subsequent iterations, the weights are changed by a small amount so that the error is reduced The process continues until the weights cannot be reduced further Classification using NN training p. prerequisite set of training pattern (many patterns) coded p. approach code the values divide set of training pattern into: – – training set training set test set build a network train the network using the training set check the network quality using the test set real data test set Business Applications of Artificial Neural Networks (ANN) Many applications across all areas of business target customers (CRM) bank loan approval hiring stock purchase trading electricity approving loan applications fraud prevention predicting bankruptcy time series forecasting Disadvantages of Neural Networks coefficients are not readily interpretable end user must apply insight in interpretation Questions!
