Deep Learning for Computer Vision with SAS

An Introduction

Robert Blanchard

• Publisher: SAS Institute
• Year: 2020
• ISBN:
  9781642959178
• Topic: Informatik
• Subtopic: Unternehmensanwendungen

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

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

Traditional Neural Networks versus Deep Learning

Deep Learning Actions

Building a Deep Neural Network

Training a Deep Learning CAS Action Model

Demonstration 1: Loading and Modeling Data with Traditional Neural Network Methods

Demonstration 2: Building and Training Deep Learning Neural Networks Using CASL Code

Introduction to Neural Networks

Artificial neural networks mimic key aspects of the brain, in particular, the brain’s 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 don’t 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.

The brain is also highly parallel and therefore very fast. It is not equivalent to one processor, but instead it is equivalent to a multitude of millions of processors, all running in parallel. Biological brains can also deal with information that is fuzzy, probabilistic, noisy, or inconsistent, all while being robust, fault tolerant, and relatively small. Although inspired by cognitive science (in particular, neurophysiology), neural networks largely draw their methods from statistical physics (Hertz et al. 1991). There are dozens, if not hundreds, of neural network algorithms.

Biological Neurons

In order to imitate neurons in artificial systems, first their mechanisms needed to be understood. There is still much to be learned, but the key functional aspects of neurons, and even small systems (networks) of neurons, are now known.

Neurons are the fun...