Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence. The complex, brain-like structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object detection and classification, and sentiment analysis.

SAS has a rich set of established and unique capabilities with regard to deep learning. This book introduces the basics of deep learning with a focus on computer vision. The book details and demonstrates how to build computer vision models using SAS software. Both the “art” and science behind model building is covered.

Is This Book for You?

The general audience for this book should be either SAS or Python programmers with knowledge of traditional machine learning methods.

What Should You Know about the Examples?

This book includes tutorials for you to follow to gain hands-on experience with SAS.

Software Used to Develop the Book’s Content

To follow along with the demos in this book, you will need the following software:

• SAS Viya (VDMML)

• SAS Studio

• Python

Example Code and Data

You can access the example code and data for this book by linking to its author page at https://support.sas.com/blanchard or on GitHub at https://github.com/sassoftware.

We Want to Hear from You

SAS Press books are written by SAS Users for SAS Users. We welcome your participation in their development and your feedback on SAS Press books that you are using. Please visit sas.com/books to do the following:

● Sign up to review a book

● Recommend a topic

● Request information on how to become a SAS Press author

● Provide feedback on a book

Do you have questions about a SAS Press book that you are reading? Contact the author through [email protected] or https://support.sas.com/author_feedback.

SAS has many resources to help you find answers and expand your knowledge. If you need additional help, see our list of resources: sas.com/books.

Learn more about this author by visiting his author page https://support.sas.com/blanchard. There you can download free book excerpts, access example code and data, read the latest reviews, get updates, and more.

About The Author

Robert Blanchard is a Senior Data Scientist at SAS where he builds end-to-end artificial intelligence applications. He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. While working at North Carolina State University, Robert also started a private analytics company that focused on predicting future home sales. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Chapter 1: Introduction to Deep Learning

Introduction to Neural Networks

Biological Neurons

Mathematical Neurons

Deep Learning

Batch Gradient Descent

Stochastic Gradient Descent

Introduction to ADAM Optimization

Weight Initialization

Regularization

Batch Normalization

Batch Normalization with Mini-Batches