This course has been replaced. Please see the schedule for the new Neural Network Modeling course.
This two-day course helps you understand and apply two popular artificial neural network algorithms, multi-layer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered. Specifically, this course teaches you how to choose an appropriate neural network architecture, how to determine the relevant training method, and how to construct custom neural networks using the NEURAL procedure.
Learn how to
- construct multilayer perceptron and radial basis function neural networks
- choose an appropriate network architecture and training method
- avoid overfitting neural networks
- perform autoregressive time series analysis using neural networks
- interpret neural network models.
Who should attend
Data analysts and modelers with a strong mathematical background
Before attending this course, you should
This course addresses SAS Enterprise Miner software.
Introduction to Neural Networks
- using the NLIN procedure for nonlinear regression
- using the REG procedure for polynomial regression
- using the GPLOT procedure for nonparametric regression
- constructing multilayer perceptrons
- constructing normalized radial basis function networks
The NEURAL Procedure
- statistical theory of error functions
- benefits and shortcomings of numerical optimization methods
- avoiding inferior local minima
- input selection using weight interpretation
- input selection using sensitivity-based pruning
The AUTONEURAL Node (Self-Study)
- defining and illustrating the use of a counterpropagation network
- defining a generalized additive neural network (GANN)
- illustrating the use of the GANN paradigm and compare its performance against other methods
- defining and illustrating how a surrogate model can be used to understand a neural network's predictions
- comparing autoneural architectures