Cover image for Deep Learning for Computer Vision with SAS

Deep Learning for Computer Vision with SAS

Author Robert Blanchard
Release Date: 2020/06/01
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Book Description

Discover deep learning and computer vision with SAS!

Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.

Readers will learn how to:

  • Define and understand deep learning
  • Build models using deep learning techniques and SAS Viya
  • Apply models to score (inference) new data
  • Modify data for better analysis results
  • Search the hyperparameter space of a deep learning model
  • Leverage transfer learning using supervised and unsupervised methods

Table of Contents

  1. Contents
  2. About This Book
  3. About The Author
  4. Chapter 1: Introduction to Deep Learning
    1. Introduction to Neural Networks
    2. Biological Neurons
    3. Deep Learning
    4. Traditional Neural Networks versus Deep Learning
    5. Building a Deep Neural Network
    6. Demonstration 1: Loading and Modeling Data with Traditional Neural Network Methods
    7. Demonstration 2: Building and Training Deep Learning Neural Networks Using CASL Code
  5. Chapter 2: Convolutional Neural Networks
    1. Introduction to Convoluted Neural Networks
    2. Input Layers
    3. Convolutional Layers
    4. Using Filters
    5. Padding
    6. Feature Map Dimensions
    7. Pooling Layers
    8. Traditional Layers
    9. Demonstration 1: Loading and Preparing Image Data
    10. Demonstration 2: Building and Training a Convolutional Neural Network
  6. Chapter 3: Improving Accuracy
    1. Introduction
    2. Architectural Design Strategies
    3. Image Preprocessing and Data Enrichment
    4. Transfer Learning Introduction
    5. Domains and Subdomains
    6. Types of Transfer Learning
    7. Transfer Learning Biases
    8. Transfer Learning Strategies
    9. Customizations with FCMP
    10. Tuning a Deep Learning Model
  7. Chapter 4: Object Detection
    1. Introduction
    2. Types of Object Detection Algorithms
    3. Data Preparation and Prediction Overview
    4. Normalized Locations
    5. Multi-Loss Error Function
    6. Error Function Scalars
    7. Anchor Boxes
    8. Final Convolution Layer
    9. Demonstration: Using DLPy to Access SAS Deep Learning Technologies: Part 1
    10. Demonstration: Using DLPy to Access SAS Deep Learning Technologies: Part 2
  8. Chapter 5: Computer Vision Case Study
  9. References
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