Search results for "Deep"
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Deep Learning with Relational Logic Representations
Deep learning has been used with great success in a number of diverse applications, ranging from image processing to game playing, and the fast progress of this learning paradigm has even been seen as paving the way towards general artificial intelligence. However, the current deep learning models are still principally limited in many ways.This book, ‘Deep Learning with Relational Logic Representations’, addresses the limited expressiveness of the common tensor-based learning representation used in standard deep learning, by generalizing it to relational representations based in mathematical logic. This is the natural formalism for the relational data omnipresent in the interlinked structures of the Internet and relational databases, as well as for the background knowledge often present in the form of relational rules and constraints. These are impossible to properly exploit with standard neural networks, but the book introduces a new declarative deep relational learning framework called Lifted Relational Neural Networks, which generalizes the standard deep learning models into the relational setting by means of a ‘lifting’ paradigm, known from Statistical Relational Learning. The author explains how this approach allows for effective end-to-end deep learning with relational data and knowledge, introduces several enhancements and optimizations to the framework, and demonstrates its expressiveness with various novel deep relational learning concepts, including efficient generalizations of popular contemporary models, such as Graph Neural Networks.Demonstrating the framework across various learning scenarios and benchmarks, including computational efficiency, the book will be of interest to all those interested in the theory and practice of advancing representations of modern deep learning architectures.
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Hardware Architectures for Deep Learning
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
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Computer Vision
This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation ofobjects, applications of object detection, and real-world challenges faced by the research community for object detection task. The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges.Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions.Topics and Features:• Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment.• Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems.• Gives detailed visual examples of applications of object detection in the real world.• Presents a detailed description of popular imaging modalities for object detection adopted by researchers.• Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.• Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures. • Discusses techniques for detection of objects in real-world applications.• Contains various hands-on practical examples and a tutorial for solving object detection problems using Python.• Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges.The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.
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Computer Vision
This comprehensive text/ book presents a broad review of both traditional (i.e., conventional) and the deep learning aspects of object detection in various adversarial conditions of real-world in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of objects, applications of object detection, and challenges of real-world faced by the research community for object detection task. The book addresses various degradations and artifacts of real-world for object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the pre-eminent researchers in the field, addressing not only the background of visibility enhancement, but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative challenges of real-world.Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging conditions of real world.Topics and Features:· Offers the first truly comprehensive presentation of aspects of the object detection in degraded and non-degraded environment.· Include in depth various degradation and artifacts, and impact of those artifacts in real-world for solving the object detection problems.· Give detailed visual examples of applications of object detection in real-world.· Presents a detailed description of popular imaging modalities adopted by the researchers for object detection.· Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks.· Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures.· Discusses techniques for detection of objects in real-world applications.· Contains various hands-on practical examples and tutorial for solving object detection problems using python.· Motivates readers to build vision based systems for solving the object detection problems in degraded and non-degraded challenges of real-world.The book will be of great interest to a broad audience ranging from researchers and practitioners, graduate and post graduate students involved in computer vision task with respect to object detection in degraded and non-degraded vision problems of real-world.
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Future Connected Technologies
The main aim of the book is to familiarize readers with the concepts of convergence of different connected and smart domains that are assisted by Cloud Computing, core technologies behind Cloud Computing, driving factors towards Cloud Computing, and security challenges and proposed solutions in Cloud Computing. The book covers not only the cloud, but also other pertinent topics such as Machine Learning, Deep Learning, IoT and Fog/Edge Computing. The last section of the book mainly focuses on the security aspects of connected technologies.The highpoints of the book is that it reviews the relation and combination of the mentioned topics, which together creates a better understanding about almost every aspect of Cloud Computing & related technologies.
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Design Studies and Intelligence Engineering
The discipline of design studies applies various technologies, from basic theory to application systems, while intelligence engineering encompasses computer-aided industrial design, human-factor design, and greenhouse design, and plays a major part within design science. Intelligence engineering technologies also include topics from theory and application, such as computational technologies, sensing technologies, and video detection.This book presents the proceedings of DSIE2023, the 2023 International Symposium on Design Studies and Intelligence Engineering, held on 28 & 29 October 2023 in Hangzhou, China. The conference provides a platform for professionals and researchers from industry and academia to present and discuss recent advances in the fields of design studies and intelligence engineering. It also fosters cooperation among the organizations and researchers involved in these overlapping fields, and invites internationally renowned professors to further explore these topics in some depth, providing the opportunity for them to discuss the technical presentations with conference participants. In all, 275 submissions were received for the conference, 105 of which were accepted after thorough review by 3 or 4 referees for presentation at the conference and inclusion here.Providing a valuable overview of the latest developments, the book will be of interest to all those working in the fields of design studies and intelligence engineering.
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Physics of Information Technology
The Physics of Information Technology explores the familiar devices that we use to collect, transform, transmit, and interact with electronic information. Many such devices operate surprisingly close to very many fundamental physical limits. Understanding how such devices work, and how they can (and cannot) be improved, requires deep insight into the character of physical law as well as engineering practice. The book starts with an introduction to units, forces, and the probabilistic foundations of noise and signalling, then progresses through the electromagnetics of wired and wireless communications, and the quantum mechanics of electronic, optical, and magnetic materials, to discussions of mechanisms for computation, storage, sensing, and display. This self-contained volume will help both physical scientists and computer scientists see beyond the conventional division between hardware and software to understand the implications of physical theory for information manipulation.
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Design Studies and Intelligence Engineering
The technologies applied in design studies vary from basic theories to more application-based systems. Intelligence engineering also plays a significant role in design sciences such as computer-aided industrial design, human factor design, and greenhouse design, and intelligent engineering technologies such as computational technologies, sensing technologies, and video detection encompass both theory and application perspectives. Being multidisciplinary in nature, intelligence engineering promotes cooperation, exchange and discussion between organizations and researchers from diverse fields.This book presents the proceedings of DSIE 2022, the International Symposium on Design Studies and Intelligence Engineering, held in Hangzhou, China, on 29 & 30 October 2022. This annual conference proves a platform for professionals and researchers from industry and academia to exchange and discuss recent advances in the field of design studies and intelligence engineering, inviting renowned experts from around the world to speak on their specialist topics, and allowing for in-depth discussion with presenters. The 189 submissions received were each carefully reviewed by 3 or 4 referees, and the 62 papers accepted for presentation and publication were selected based on their scores. Papers cover a very wide range of topics, from the design of a bachelor apartment, or a children’s backpack for healthy spine development, to interpretable neural symbol learning methods and design elements extraction from point-cloud datasets using deep enhancement learning.Offering a varied overview of recent developments in design and intelligence engineering, this book will be of interest to all those working in the field.
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Mastering Azure Machine Learning
Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning servicesKey FeaturesImplement end-to-end machine learning pipelines on AzureTrain deep learning models using Azure compute infrastructureDeploy machine learning models using MLOpsBook DescriptionAzure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline.What you will learnUnderstand the end-to-end ML pipelineGet to grips with the Azure Machine Learning workspaceIngest, analyze, and preprocess datasets for ML using the Azure cloudTrain traditional and modern ML techniques efficiently using Azure MLDeploy ML models for batch and real-time scoringUnderstand model interoperability with ONNXDeploy ML models to FPGAs and Azure IoT EdgeBuild an automated MLOps pipeline using Azure DevOpsWho this book is forThis book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.
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Real World Windows 10 Development
Emphasizing XAML and C#, this book provides readers with all the tools, ideas, and inspiration to begin Windows Universal App development for Windows 10.Real World Windows 10 Development addresses developers who want to break into this market by providing detailed explanations of the various aspects of Universal App development. Written by authors with deep knowledge in Windows 10 universal app development, you will learn how to make the most of the Windows 10 SDK to build applications that can be published on IoT devices, phones, tablets, laptops, desktops, Xbox, HoloLens, and the Surface Hub. Readers will learn how to:Extend the appeal of their native, web-based, or universal apps with media, shell integration, and inter-app communicationsBuild adaptive user interfaces that scale to the screen dimensions they are displayed onMonitize your appsIntroduce mappingWhat if you already have pre-existing software in the form of native win32 applications or a website? Real World Windows 10 Development tackles this by providing detailed tutorials on the approaches used to leverage your existing code investment.Finally, Real World Windows 10 Development provides a step-by-step walk through of the various approaches developers can use to distribute their universal apps.In this book, you’ll get: Detailed descriptions of Windows 10 app developmentSamples emphasizing the use of XAML/C#Adherence to Windows 10 guidelines for successful app acceptance
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