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Deep Learning Using SAS(R) Software

Course Details
Code: DLUS3I
Tuition (USD): $650.00 • Classroom (1 day)
$650.00 • Virtual (1 day)

This course introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes. The course also presents practical methods used to enhance training data to produce better models. Lastly, a method for efficiently searching hyperparameters is described.

Skills Gained

  • Define and understand deep learning.
  • Build models using deep learning techniques.
  • Apply models to score (inference) new data.
  • Modify data for better analysis results.
  • Search the hyperparameter space of a deep learning model.

Who Can Benefit

  • Machine learners and those interested in deep learning

Prerequisites

  • Before attending this course, you should be acquainted with Microsoft Windows and Windows software. In addition, you should have at least an introductory-level familiarity with basic neural network modeling ideas. You can gain this neural network modeling knowledge by completing either the Introduction to Neural Networks in SAS(R) or the Neural Network Modeling course. Previous SAS software experience is helpful but not required.

Course Details

Introduction to Deep Learning

  • Introduction to deep learning.
  • Autoencoders.
  • Building level-specific autoencoders (self-study).

Convolutional Neural Networks

  • Applications.
  • Input layers.
  • Convolutional layers.
  • Padding.
  • Pooling layers.
  • Traditional layers.
  • Types of skip-layer connections.
  • Image pre-processing and data enrichment.

Recurrent Neural Networks

  • Introduction.
  • Recurrent neural networks overview.
  • Sub-types of recurrent neural networks.

Tuning a Neural Network

  • Selecting hyperparameters.