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Neural Networks

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

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Neural Networks

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  1. Neural Networks Week 5

  2. Applications • Predict the taste of Coors beer as a function of its chemical composition

  3. 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

  4. 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

  5. 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

  6. Basic Concepts of Neural Networks

  7. Basic Concepts of Neural Networks

  8. Relationship Between Biological and Artificial Neural Networks • Soma – Node • Dendrites – Input • Axon – Output • ANNs typically have much fewer neurons than humans

  9. Basic Concepts of Neural Networks • Network structure (three layers) • Input • Intermediate (hidden layer) • Output

  10. Basic Concepts of Neural Networks

  11. 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)/(1-exp(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

  12. Neural Network Prediction Formula 1 tanh 0 -5 5 -1 hidden unit bias estimate weight estimate prediction estimate activation function ... ...

  13. Neural Network Binary Prediction Formula 1 5 tanh 0 -5 5 0 1 -1 -5 logit link function ...

  14. 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

  15. Learning in ANN

  16. 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

  17. Learning in ANN • How a network learns • Procedure for a learning algorithm • Initialize weights with random values and set other parameters • Read in the input vector and the desired output • Compute the actual output via the calculations, working forward through the layers • Compute the error • Change the weights by working backward from the output layer through the hidden layers

  18. Learning in ANN

  19. 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

  20. Classification using NN training p. prerequisite • set of training pattern (many patterns) approach • code the values • divide set of training pattern into: • training set • test set • build a network • train the network using the training set • check the network quality using the test set coded p. test set training set real data

  21. 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

  22. Disadvantages of Neural Networks • coefficients are not readily interpretable • end user must apply insight in interpretation

  23. Questions!

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