Robert Blanchard - Deep Learning for Computer Vision with SAS

The correct bibliographic citation for this manual is as follows: Blanchard, Robert 2020. Deep Learning for Computer Vision with SAS : An Introduction . Cary, NC: SAS Institute Inc.

Deep Learning for Computer Vision with SAS: An Introduction

Copyright 2020, SAS Institute Inc., Cary, NC, USA

ISBN 978-1-64295-972-7 (Hardcover)
ISBN 978-1-64295-915-4 (Paperback)
ISBN 978-1-64295-916-1 (PDF)
ISBN 978-1-64295-917-8 (EPUB)
ISBN 978-1-64295-918-5 (Kindle)

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Contents

About This Book

What Does This Book Cover?

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 Books 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 .

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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 masters degree in Business Analytics and Project Management from the University of Connecticut and a masters degree in Applied and Resource Economics from East Carolina University.

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.

Chapter 1: Introduction to Deep Learning

Introduction to Neural Networks

Artificial neural networks mimic key aspects of the brain, in particular, the brains ability to learn from experience. In order to understand artificial neural networks, we first must understand some key concepts of biological neural networks, in other words, our own biological brains.

A biological brain has many features that would be desirable in artificial systems, such as the ability to learn or adapt easily to new environments. For example, imagine you arrive at a city in a country that you have never visited. You dont know the culture or the language. Given enough time, you will learn the culture and familiarize yourself with the language. You will know the location of streets, restaurants, and museums.