Neural Network Modeling

Course code: DMNN5

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.

The e-learning format of this course includes Virtual Lab time to practice.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 14 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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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.


  • 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.


  • DMDB procedure.
  • NEURAL procedure.

Empirical Partial Residuals

  • Generating empirical partial residual plots to guide variable selection.


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.
  • Do you need advice or a tailor-made course?


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