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Books in Machine learning

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    • Essential Kubeflow

      • 1st Edition
      • May 1, 2026
      • Prashanth Josyula + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 5 4 3
      • eBook
        9 7 8 0 4 4 3 4 5 2 5 5 0
      Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

    • Artificial Intelligence and Machine Learning for Safety-Critical Systems

      • 1st Edition
      • May 1, 2026
      • Rajiv Pandey + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 9 7 3
      • eBook
        9 7 8 0 4 4 3 3 6 5 9 8 0
      Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

    • Grey Wolf Optimizer

      • 1st Edition
      • May 1, 2026
      • Seyedali Mirjalili
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 6 2 4 6
      • eBook
        9 7 8 0 4 4 3 3 6 6 2 5 3
      Grey Wolf Optimizer: A Pack of Solutions for Your Optimization Problems offers in-depth coverage of recent theoretical advancements in GWO, as well as several variants, improvements, and hybrid approaches developed to enhance the GWO's performance and adaptability. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. The GWO algorithm is an influential and rapidly advancing metaheuristic algorithm that has gained substantial attention across scientific and industrial domains. However, solving optimization problems using the GWO involves addressing various challenges, including but not limited to: handling multiple objectives, managing constraints, working with binary decision variables, navigating large-scale search spaces, adapting to dynamic objective functions, and dealing with noisy or uncertain parameters. This book directly addresses these needs by providing a thorough exploration of the GWO, offering a deep dive into the algorithm's foundations and presenting new developments to help researchers overcome common challenges. The book features numerous case studies and real-world examples across various fields, such as engineering, healthcare, finance, and environmental management. These applications demonstrate the versatility and effectiveness of the GWO in addressing complex, interdisciplinary challenges, making the content highly relevant and practical for readers. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an essential resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

    • Understanding Models Developed with AI

      • 1st Edition
      • May 1, 2026
      • Ömer Faruk Ertuğrul + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 1 6 3 9
      • eBook
        9 7 8 0 4 4 3 4 4 1 6 4 6
      Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide for readers looking to understand the intricacies of AI models and their real-world applications. This book demystifies complex AI methodologies by providing clear explanations and practical examples, reinforced with Python and MATLAB program code. It is an essential resource for readers who aim to develop and interpret AI models effectively. The primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results and bias (data and algorithm) management. Researchers and developers need to be able to not only implement AI models, but also to interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable. This book is a valuable reference that equips readers with the tools to build AI models along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems. The book’s content structure emphasizes a practical, application-driven approach to understanding AI models, with hands-on coding examples throughout each chapter.

    • Smart Wearable IoT

      • 1st Edition
      • May 1, 2026
      • Shuenn-Yuh Lee + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 7 0 0 7
      • eBook
        9 7 8 0 4 4 3 3 6 7 0 1 4
      Smart Wearable IoT: Principles and Implementation of Development Modules with Wireless Biomedical SoC focuses on the development of intelligent wearable technology integrated with the Internet and various platforms. The book provides detailed guidance on building a user-friendly development platform that features intelligent wearable systems, including bio-signal SoCs/modules, user-friendly websites/apps, and artificial intelligence (AI) systems on edge/cloud. Through wireless bio-signal acquisition, readers can continuously access and monitor their vital signs via the wearable platform. By exploring specific case studies, such as the ECG-based fatigue analysis system, readers will gain fundamental knowledge in biosignal acquisition and processing. This hands-on approach enables them to understand the integration of digital signal processing and artificial intelligence in analyzing physiological data, ultimately enhancing their skills in developing innovative wearable solutions.

    • Distributed AI in the Modern World

      • 1st Edition
      • May 1, 2026
      • Andrei Olaru + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 6 7 9 5
      • eBook
        9 7 8 0 4 4 3 4 4 6 8 0 1
      Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents several state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. This book does not analyze the internal workings of machine learning models (for instance, in the case of multi-agent reinforcement learning), but instead provides readers with an overview of the challenges brought by the need of artificially intelligent entities to interact with other entities and with their environment along with practical solutions at an architectural level. Deployment, maintenance and monitoring of distributed machine learning systems brings about many practical challenges, dealing with the intelligent agents distributed across a network of heterogenous devices, or interacting with robots and humans alike. While these scenarios are very different, some challenges remain the same when interaction exists: discoverability, availability, communication language and formats, and efficiency in transferring significant amounts of information. The book provides readers with practical solutions at an architectural level, with solutions presented in three parts. Part 1 deals with the distribution of the learning process and the utilization of machine learning models in a distributed system. Part 2 deals with tools that enable the distribution and interaction of artificial learning entities and how multi-agent systems and machine learning can be combined. Part 3 deals with the physical embodiment of intelligent agents and the interaction of intelligent computing units bound to physical space. The three parts are followed by a conclusion, emphasizing the challenges that are common to all scenarios and solutions which apply in a wider range of cases.

    • Advanced Intelligence Methods for Data Science and Optimization

      • 1st Edition
      • April 1, 2026
      • Amir Hossein Gandomi + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 8 9 4 0 8
      • eBook
        9 7 8 0 4 4 3 2 8 9 4 1 5
      Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

    • The Governance of Artificial Intelligence

      • 1st Edition
      • April 1, 2026
      • Tshilidzi Marwala
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 3 2 2 1
      • eBook
        9 7 8 0 4 4 3 3 6 3 2 3 8
      Artificial Intelligence governance is complicated by the fast pace of technological progress, the opaqueness of AI algorithms, worries about bias and impartiality, the requirement for accountability in AI-based decisions, and the global nature of AI development and deployment. The issue of AI governance is a developing one, and The Governance of AI is the first book that covers all the key topics in one book: AI values, Data, Algorithms, Computing, Applications, and Governance. The Governance of AI provides top-level guidance on all these topics from an engineering and governance perspective, while proposing a unifying framework for AI governance. An essential approach to AI governance is a proactive and comprehensive strategy that efficiently balances innovation and ethical concerns. The strategy presented in this book prioritizes social welfare and upholds human rights by maximizing the benefits of AI while reducing its negative aspects. In order to address these issues, it is essential to implement a versatile governance structure that incorporates several fields of study and encourages diversity. Additionally, utilizing existing regulatory frameworks, ethical standards, and industry benchmarks is essential. Moreover, it is crucial to integrate cooperation between governments, economic organizations, civil society, and the academic community under a multi-stakeholder framework to promote transparency, accountability, and public trust in AI systems. Furthermore, it is imperative to cultivate global cooperation in regulating AI because AI technology and its impacts extend beyond national boundaries. AI governance involves establishing worldwide norms and standards that encourage coordinating governance efforts while recognizing cultural and geographical differences. The Governance of AI is structured into six distinct sections and comprises 33 chapters. The first section comprises the chapters that address the principles that govern artificial AI. The second section has chapters that specifically address data-related topics. The AI algorithms are discussed in the third section. The fourth section has chapters that address the issue of computing. The fifth section has chapters that specifically address applications. The sixth section has chapters that address the topic of AI governance.

    • Digital Supply Chain Transformation

      • 1st Edition
      • April 1, 2026
      • Vinaytosh Mishra
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 9 9 5 0
      • eBook
        9 7 8 0 4 4 3 3 3 9 9 6 7
      Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the role of digital transformation in modern supply chains. Through a blend of theoretical learning and practical applications, readers will gain a deep understanding of foundational supply chain principles while exploring emerging trends and technologies reshaping the industry. Topics such as system dynamics modelling, machine learning, artificial intelligence, and end-to-end visibility will be explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. There is a growing recognition that supply chain management can be significantly improved by leveraging modern technologies, such as machine learning and generative AI, to enhance efficiency and accuracy. Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions helps readers:Comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries.Recognize the significance of digital transformation in supply chains, understanding the tools, technologies, and strategies essential for a successful transformation.Evalu... the importance of end-to-end supply chain visibility and employ methods and technologies to enhance this visibility in practical scenarios.Apply system dynamics modelling techniques to address complex supply chain problems and optimize supply chain processes.Utilize supply chain analytics and tools to make data-driven decisions, enhancing efficiency and reducing operational costs.Understand the potential of machine learning and artificial intelligence in supply chains, applying these technologies to innovate and solve real-world supply chain challenges.Identify and analyze emerging trends in supply chain management, anticipating future challenges and opportunities in the field.Demonstrate the ability to apply theoretical knowledge to practical scenarios, devising solutions for real-world supply chain challenges.Criticall... evaluate supply chain strategies, technologies, and solutions, recommending improvements and innovations based on informed analysis.

    • The AI Ideal

      • 1st Edition
      • April 1, 2026
      • Niklas Lidströmer
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 9 7 2 7
      • eBook
        9 7 8 0 4 4 3 4 4 9 7 3 4
      The AI Ideal: Aidealism and the Governance of AI, Dr. Niklas Lidströmer presents a bold alternative to the prevailing AI doom narratives. While nearly all other experts focus solely on warning of catastrophe, Dr Lidströmer also offers an actionable vision for ensuring AI strengthens democracy, ethics, and human dignity. Instead of allowing AI to concentrate power in the hands of a few, he argues for a new global framework—one where AI serves justice, enlightenment, and human betterment. Rooted in European Enlightenment ideals, Scandinavian social model and liberalism, and Swiss direct democracy, Aidealism rejects extreme ideologies and champions pragmatic, ethical, and forward-thinking solutions. From free education and healthcare to AI-driven economic justice and climate responsibility, this book explores how AI can help build a sustainable, free, and prosperous world—if we act now. Yet Aidealism does not promise utopia. The risks are real. The threats are mounting. AI could empower autocrats, disrupt economies, and undermine human agency. But it could also be our greatest tool for wisdom, fairness, and progress—if governed with foresight and courage. This book explicitly gives a manifesto for practical action. An action plan for how to harness and use AI for the common good, so that it benefits us all, rather than the few. The book elaborates on the daily conundrums of the human species; our nature, origins, goodness and cruelty, memes, hierarchies, political structures and how to build a fairer, more just, peaceful and benevolent society. It also tries to explain the core of AI for the general audience. It delves into a very broad range of areas, from philosophy to music, politics to ethics, and mathematics and physics to sociology and medicine. It tries to usher in an technological Enlightenment to save us from the threat of a malign and Machiavellian use of AI. This is not another AI dystopia, nor is it blind optimism. It is a manifesto for action—a call to use AI not just to enhance intelligence, but to make humanity nobler. The AI revolution is not something happening to us—it is something happening through us. The only question is: Will we build wisely? For those seeking a visionary, constructive, and ethically grounded roadmap for AI—one written in the spirit of true idealism—this book is essential reading.