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.
