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Practice JMP using these webinar videos and resources. We hold live Mastering JMP Zoom webinars with Q&A most Fridays at 2 pm US Eastern Time. See the list and register. Local-language live Zoom webinars occur in the UK, Western Europe and Asia. See your country site.

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Discovering and Predicting Patterns Using Neural Network Models


See how to:

  • Understand a neural network as a function of a set of derived inputs, called hidden nodes, that are nonlinear functions of the original inputs
  • Interpret Neural Network diagram inputs (factors) and outputs (responses) 
  • Understand terms and how they apply to building Neural Networks (nodes, activation type, activation functions)
  • Understand types of activation functions  (TanH, Linear, Gaussian, Sigmoid, Identity and Radial) that transform a linear combination of the X variables at the nodes of hidden layers
  • Follow sequence for building a Neural Network model 
    • Launch model (JMP vs. JMP Pro)
    • Specify # nodes in first and second layer
    • Use boosting to build a larger additive model fit on the scaled residuals of previous smaller models)
    • Specify Learning Rate (closer to 1 will run model faster, but may tend to overfit)
    • Transform covariates
    • Use Robust Fit for continuous output to minimize impact of outliers
    • Understand how JMP uses penalty methods and number of tours
    • Select Penalty Method (Squared, Absolute, No Penalty) to help avoid overfitting
    • Select number of Tours
  • Case study building a neural network for a continuous response
    • Use JMP validation to avoid overfitting
    • Compare Training set and Validation set values
    • Use Model Launch to change Neural Net settings
    • Use Actual by Predicted Plot and Profiler to evaluate how predictors impact results
  • Case study building a neural network for categorical response 
    • Interpret Fit Statistics, Confusion Matrix, ROC Curves and Lift Curves



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