Neural Networks: Essentials

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.

Learn how to

• Programmatically build neural networks in SAS 9.4 and SAS Viya.
• Modify neural networks' parameters for better performance.
• Conduct automatic search for neural networks' hyperparameters through genetic algorithm.
• Enhance data with autoencoders and synthetic observations.

Who should attend

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

Formats available	Duration
Live Web Classroom:	3 half-day session(s)
e-Learning:	10.5 hours/180 day license

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.

This course addresses SAS Viya software.

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)