Deep Learning in Visual Computing and Signal Processing

An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more. "This new volume, Deep Learning in Visual Computing and Signal Processing, covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more. Key features: Covers both the fundamentals and the latest concepts in deep learning; presents some of the diverse applications of deep learning in visual computing and signal processing; includes over 90 figures and tables to elucidate the text. An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this valuable resource will serve as a guide for researchers, engineers, and students who want to have a quick start on learning and/or building deep learning systems. It provides a good theoretical and practical understanding and complete information and knowledge required to understand and build deep learning models from scratch."-- Provided by publisher. This New Volume, Deep Learning In Visual Computing And Signal Processing, Covers The Fundamentals And Advanced Topics In Designing And Deploying Techniques Using Deep Architectures And Their Application In Visual Computing And Signal Processing. The Volume First Lays Out The Fundamentals Of Deep Learning As Well As Deep Learning Architectures And Frameworks. It Goes On To Discuss Deep Learning In Neural Networks And Deep Learning For Object Recognition And Detection Models. It Looks At The Various Specific Applications Of Deep Learning In Visual And Signal Processing, Such As In Biorobotics, For Automated Brain Tumor Segmentation In Mri Images, In Neural Networks For Use In Seizure Classification, For Digital Forensic Investigation Based On Deep Learning, And More. Key Features: Covers Both The Fundamentals And The Latest Concepts In Deep Learning, Presents Some Of The Diverse Applications Of Deep Learning In Visual Computing And Signal Processing, And Includes Over 90 Figures And Tables To Elucidate The Text. An Enlightening Amalgamation Of Deep Learning Concepts With Visual Computing And Signal Processing Applications, This Valuable Resource Will Serve As A Guide For Researchers, Engineers, And Students Who Want To Have A Quick Start On Learning And/or Building Deep Learning Systems. It Provides A Good Theoretical And Practical Understanding And Complete Information And Knowledge Required To Understand And Build Deep Learning Models From Scratch-- Cover Half Title Title Page Copyright Page About the Editors Table of Contents Contributors Abbreviations Preface 1. Deep Learning Architecture and Framework 2. Deep Learning in Neural Networks: An Overview 3. Deep Learning: Current Trends and Techniques 4. TensorFlow: Machine Learning Using Heterogeneous Edge on Distributed Systems 5. Introduction to Biorobotics: Part of Biomedical Signal Processing 6. Deep Learning-Based Object Recognition and Detection Model 7. Deep Learning: A Pathway for Automated Brain Tumor Segmentation in MRI Images 8. Recurrent Neural Networks and Their Application in Seizure Classification 9. Brain Tumor Classification Using Convolutional Neural Network 10. A Proactive Improvement Toward Digital Forensic Investigation Based on Deep Learning Index Covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. It discusses deep learning in neural networks and for object recognition and detection models and the specific applications in visual and signal processing.