Convolutional Neural Networks in Visual Computing_A Concise Guide-CRC(2018).pdf

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Deep learning architectures have attained incredible popularity in recent years due to their phenomenal success in, among other appli- cations, computer vision tasks. Particularly, convolutional neural networks (CNNs) have been a signi cant force contributing to state- of-the-art results. e jargon surrounding deep learning and CNNs can often lead to the opinion that it is too labyrinthine for a beginner to study and master. Having this in mind, this book covers the funda- mentals of deep learning for computer vision, designing and deploying CNNs, and deep computer vision architecture. is concise book was intended to serve as a beginner’s guide for engineers, undergraduate seniors, and graduate students who seek a quick start on learning and/ or building deep learning systems of their own. Written in an easy- to-read, mathematically nonabstruse tone, this book aims to provide a gentle introduction to deep learning for computer vision, while still covering the basics in ample depth. e core of this book is divided into ve chapters. Chapter 1 pro- vides a succinct introduction to image representations and some com- puter vision models that are contemporarily referred to as hand-carved. e chapter provides the reader with a fundamental understanding of image representations and an introduction to some linear and non- linear feature extractors or representations and to properties of these representations. Onwards, this chapter also demonstrates detection xi xii PrefaCe of some basic image entities such as edges. It also covers some basic machine learning tasks that can be performed using these representa- tions. e chapter concludes with a study of two popular non-neural computer vision modeling techniques. Chapter 2 introduces the concepts of regression, learning machines, and optimization. is chapter begins with an introduction to super- vised learning. e rst learning machine introduced is the linear regressor. e rst solution covered is the analytical solution

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