Deep Learning Using SAS(R) Software

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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.