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.
Implement neural networks in a distributed computing environment.
Who Should Attend
Data analysts and modelers with a strong mathematical background
Prerequisites
Before attending this course, you should:;
Have an understanding of basic statistical concepts, which you can gain from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
Have completed the SAS Programming 1: Essentials course or have equivalent knowledge.
Be familiar with SAS Enterprise Miner software. You can gain this knowledge from the Applied Analytics Using SAS Enterprise Miner course.
Have completed a college-level calculus course.
SAS Products Covered
SAS Enterprise Miner
Course Outline
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.
This course helps prepare you to earn the following SAS Credentials:
