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Wiley-VCH - Computer Science
A Passion for Publishing
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Computational Physics
Fourth edition of a classic on computational physics with new chapters on data science, machine learning and general relativity, plus a plethora of supplementary materials. [592 Pages, Softcover]
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Supervised and Unsupervised Data Engineering for Multimedia Data
SUPERVISED and UNSUPERVISED DATA ENGINEERING for MULTIMEDIA DATA Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency. Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies. The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you're a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age. [336 Pages, Hardcover]
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The Cybersecurity Guide to Governance, Risk, and Compliance
The Cybersecurity Guide to Governance, Risk, and Compliance Understand and respond to a new generation of cybersecurity threats Cybersecurity has never been a more significant concern of modern businesses, with security breaches and confidential data exposure as potentially existential risks. Managing these risks and maintaining compliance with agreed-upon cybersecurity policies is the focus of Cybersecurity Governance and Risk Management. This field is becoming ever more critical as a result. A wide variety of different roles and categories of business professionals have an urgent need for fluency in the language of cybersecurity risk management. The Cybersecurity Guide to Governance, Risk, and Compliance meets this need with a comprehensive but accessible resource for professionals in every business area. Filled with cutting-edge analysis of the advanced technologies revolutionizing cybersecurity, increasing key risk factors at the same time, and offering practical strategies for implementing cybersecurity measures, it is a must-own for CISOs, boards of directors, tech professionals, business leaders, regulators, entrepreneurs, researchers, and more. The Cybersecurity Guide to Governance, Risk, and Compliance also covers: * Over 1300 actionable recommendations found after each section * Detailed discussion of topics including AI, cloud, and quantum computing * More than 70 ready-to-use KPIs and KRIs "This guide's coverage of governance, leadership, legal frameworks, and regulatory nuances ensures organizations can establish resilient cybersecurity postures. Each chapter delivers actionable knowledge, making the guide thorough and practical." --GARY MCALUM, CISO "This guide represents the wealth of knowledge and practical insights that Jason and Griffin possess. Designed for professionals across the board, from seasoned cybersecurity veterans to business leaders, auditors, and regulators, this guide integrates the latest technological insights with governance, risk, and compliance (GRC)". --WIL BENNETT, CISO [672 Pages, Hardcover]
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Metaheuristics for Machine Learning
METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You'll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms. [352 Pages, Hardcover]
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Softwaretesten nach ISTQB CTFL 4.0 für Dummies
Dieses Buch liefert Ihnen das notwendige Handwerkszeug zum Softwaretesten und die notwendige Sicherheit zum Bestehen des ISTQB® Certified Tester Foundation Level in der aktuellen Version 4. [384 Pages, Softcover]
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