Neural Networks: Essentials

Course code: INTN35

This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

and certified lecturers

recognized certifications

Wide range of technical
and soft skills courses

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 10h 30min

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 21 hours en 720 EUR Register
Upon request Upon request 10h 30min en 1 200 EUR Register
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Target group

Those interested in learning about neural networks, general machine learning and data science techniques, and SAS software

Course structure

Neural Networks: Essentials

  • Introduction.
  • Multilayer perceptrons.
  • Neural network modeling paradigm.
  • Using a surrogate model to interpret neural network predictions.
  • Other considerations.

Neural Network Details

  • Parameter estimation.
  • Numerical optimization methods.
  • Regularization.
  • Unbalanced data.
  • SAS search optimizations (self-study).

Tuning a Neural Network

  • Selecting hyperparameters with autotuning.

Introduction to Deep Learning

  • Introduction to deep learning.
  • Autoencoders.

Radial Basis Function Networks (Self-Study)


Before taking this course, you should have the following:
  • Some familiarity with programming in SAS or SQL (or both).
  • An understanding of predictive modeling.
  • A basic understanding of calculus.
  • Do you need advice or a tailor-made course?


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