Target group
Data analysts and modelers with a strong mathematical background
Course structure
Introduction to Neural Networks
- Provide a brief history of neural networks.
- Describe key concepts underlying neural networks.
- Illustrate traditional approaches to nonlinear modeling.
Network Architecture
- Define the linear perceptron neural network.
- Describe combination and activation functions.
- Show how a linear perceptron is a generalized linear model that is able to model many target distributions.
- Detail multilayer and skip-layer perceptrons.
- Detail ordinary and normalized radial basis functions.
Learning
- Describe the problem of local minima.
- Describe the parameter estimation methods.
- Outline the optimization (training) techniques that are available in the Neural Network node.
NEURAL Procedure
- Overview of PROC NEURAL.
- Input selection using PROC NEURAL.
- Define sequential network construction (SNC).
- Illustrate the SNC paradigm.
- Stochastic gradient descent.
Augmented Networks
- Implementing a time delay neural network.
- Interpreting a neural network with a continuous target.
- Interpreting a neural network with a categorical target.
HP Neural Node
- Outline the challenge of big data.
- Introduce SAS High-Performance Analytics.
- Describe the HP Neural node’s interface.
PROC DMDB and PROC NEURAL User’s Guide
- DMDB procedure.
- NEURAL procedure.
Empirical Partial Residuals
- Generating empirical partial residual plots to guide variable selection.
