Deep Learning Using SAS(R) Software

Course code: DLUS35

This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, recurrent networks, and variants of denoising autoencoders. The neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Transfer learning is covered because the emergence of this field has shown promise in deep learning. Lastly, you learn how to customize a SAS deep learning model to research new areas of deep learning.

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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

Machine learners and those interested in deep learning, computer vision, or natural language processing

Course structure

Introduction to Deep Learning

  • Introduction to neural networks.
  • Introduction to deep learning.
  • ADAM optimization.
  • Dropout.
  • Batch normalization.
  • 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.
  • Training convolutional neural networks.

Recurrent Neural Networks

  • Introduction.
  • Recurrent neural networks overview.
  • Sub-types of recurrent neural networks.
  • Time series analysis using recurrent neural networks.
  • Sentiment analysis using recurrent neural networks.

Tuning a Neural Network

  • Selecting hyperparameters.
  • Hyperband.

Additional Topics

  • Types of transfer learning.
  • Transfer learning basics.
  • Transfer learning strategies.
  • Transfer learning with unsupervised pretraining.
  • Customizations with FCMP.


Before attending this course, 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 Neural Networks: Essentials or Neural Network Modeling course. Previous SAS software experience is helpful but not required.

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