Deep Learning for Computer Vision with SAS. Robert Blanchard
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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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June 2020
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Contents
Chapter 1: Introduction to Deep Learning
Introduction to Neural Networks
Traditional Neural Networks versus Deep Learning
Building a Deep Neural Network
Demonstration 1: Loading and Modeling Data with Traditional Neural Network Methods
Demonstration 2: Building and Training Deep Learning Neural Networks Using CASL Code
Chapter 2: Convolutional Neural Networks
Introduction to Convoluted Neural Networks
Input Layers
Convolutional Layers
Using Filters
Padding
Feature Map Dimensions
Pooling Layers
Traditional Layers
Demonstration 1: Loading and Preparing Image Data
Demonstration 2: Building and Training a Convolutional Neural Network
Introduction
Architectural Design Strategies
Image Preprocessing and Data Enrichment
Transfer Learning Introduction
Domains and Subdomains
Types of Transfer Learning
Transfer Learning Biases
Transfer Learning Strategies
Customizations with FCMP
Tuning a Deep Learning Model
Introduction
Types of Object Detection Algorithms
Data Preparation and Prediction Overview
Normalized Locations
Multi-Loss Error Function
Error Function Scalars
Anchor Boxes
Final Convolution Layer
Demonstration: Using DLPy to Access SAS Deep Learning Technologies: Part 1
Demonstration: Using DLPy to Access SAS Deep Learning Technologies: Part 2
Chapter 5: Computer Vision Case Study
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