Convolutional Neural Networks in Visual Computing: A Concise Guide (Data-Enabled Engineering)

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics. "Cover"--"Half tilte"--"Series title" -- "Title page" -- "Copyright" -- "Dedication" -- "Contents" -- "Preface" -- "Acknowledgments" -- "Authors" -- "Chapter 1: Introduction to Visual Computing" -- " Image Representation Basics" -- "Transform-Domain Representations" -- " Image Histograms" -- " Image Gradients and Edges" -- "Going beyond Image Gradients" -- " Line Detection Using the Hough Transform" -- " Harris Corners" -- "Scale-Invariant Feature Transform" -- " Histogram of Oriented Gradients" -- "Decision-Making in a Hand-Crafted Feature Space" -- "Bayesian Decision-Making" -- "Decision-Making with Linear Decision Boundaries" -- "A Case Study with Deformable Part Models" -- "Migration toward Neural Computer Vision" -- "Summary" -- "References" -- "Chapter 2: Learning As a Regression Problem" -- "Supervised Learning" -- "Linear Models" -- "Least Squares" -- "Maximum-Likelihood Interpretation" -- "Extension to Nonlinear Models" -- "Regularization" -- "Cross-Validation" -- "Gradient Descent" -- "Geometry of Regularization" -- "Nonconvex Error Surfaces" -- "Stochastic, Batch, and Online Gradient Descent" -- "Alternative Update Rules Using Adaptive Learning Rates" -- "Momentum" -- "Summary" -- "References" -- "Chapter 3: Artificial Neural Networks" -- "The Perceptron" -- " Multilayer Neural Networks" -- "The Back-Propagation Algorithm" -- "Improving BP- Based Learning" -- " Activation Functions" -- " Weight Pruning" -- " Batch Normalization" -- "Summary" -- "References" -- "Chapter 4: Convolutional Neural Networks" -- "Convolution and Pooling Layer" -- "Convolutional Neural Networks" -- "Summary" -- "References" -- "Chapter 5: Modern and Novel Usages of CNNs" -- "Pretrained Networks" -- "Generality and Transferability" -- "Using Pretrained Networks for Model Compression" -- "Mentee Networks and FitNets Content: DedicationAcknowledgementsAbout the AuthorPreface Chapter 1: Introduction to visual computingChapter 2: Learning as a regression problemChapter 3: Artificial neural networksChapter 4: Convolutional neural networksChapter 5: Modern and novel usages of CNNsAppendixPostscript