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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 jmp.com/mastering site.
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