Neural Network Modeling

Target group

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.