Deep Learning for Computer Vision with SAS

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