This 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, how to implement neural network models in a distributed computing environment, and how to construct custom neural networks using the NEURAL procedure.
- 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 Can Benefit
- Data analysts and modelers with a strong mathematical background
- Before attending this course, you should
- have an understanding of basic statistical concepts, which you can gain from the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.
- have completed the SAS(R) Programming I: Essentials course or have equivalent knowledge.
- be familiar with SAS Enterprise Miner software. You can gain this knowledge from the Applied Analytics Using SAS(R) Enterprise Miner(TM) 5.2 course.
- have completed a college-level calculus course.