Search results for "Python for Generative AI"
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Understanding the Ethical Concerns of Generative AI
This textbook is designed to help readers navigate a world increasingly shaped by generative AI technologies. It prepares readers to confront issues such as bias, sustainability, transparency, and privacy as these technologies become more ubiquitous in daily life. Emphasis is placed on developing a theoretical foundation for ethical behavior and understanding the new ethical concerns that emerge from the use of generative AI.
R 1,217.50
R 844.00
R 550.00
Hybrid Intelligence
Uniquely aimed at teams that think together to solve problems and make decisions, this book explains how to enhance the collective intelligence of a team-size group and combine it with the artificial intelligence (AI) of generative AI to create a hybrid intelligence that is smarter than either one on its own.Boards, committees, and other team-size groups of 5-20 people are the primary problem-solving and decision-making units within organizations, and they form the bridges between organizations, industries, and nations that collaborate on projects. So how can leaders exponentially improve their teams’ capabilities? Assemble the right people, arm them with the right processes, and execute those processes on the right platforms—then add generative AI to enhance those practices. This book describes and synthesizes various ways of increasing a group’s collective intelligence through people, process, and platform practices, and goes on to explain how to augment the practices with generative AI, including how to ask it questions and what questions to ask to obtain superior answers. The resulting upsurge in team capability enables organizations to survive and thrive in a world that is growing progressively more competitive and complex.This book will become the definitive resource for leaders and managers of commercial, government, and nonprofit organizations who want to learn how to significantly improve their teams’ problem-solving and decision-making ability by increasing their collective intelligence and combining it with the artificial intelligence of generative AI.
R 9,324.22
R 611.00
R 733.00
R 610.00
R 540.51
Advances in Deep Generative Models for Healthcare and Medical Applications
This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.
R 7,271.38
R 657.00
R 489.00
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R 641.00
Generative AI for Web Engineering Models
Web engineering faces a pressing challenge in keeping pace with the rapidly evolving digital landscape. Developing, designing, testing, and maintaining web-based systems and applications require innovative approaches to meet the growing demands of users and businesses. Generative Artificial Intelligence (AI) emerges as a transformative solution, offering advanced capabilities to enhance web engineering models and methodologies. This book presents a timely exploration of how Generative AI can revolutionize the web engineering discipline, providing insights into future challenges and societal impacts. Generative AI for Web Engineering Models offers a comprehensive examination of integrating AI-driven generative approaches into web engineering practices. It delves into methodologies, models, and the transformative impact of Generative AI on web-based systems and applications. By addressing topics such as web browser technologies, website scalability, security, and the integration of Machine Learning, this book provides a roadmap for researchers, scientists, postgraduate students, and AI enthusiasts interested in the intersection of AI and web engineering. Through insightful analysis and real-world examples, this book equips readers with the knowledge and tools to navigate the complexities of web engineering in the AI era. It offers practical strategies for leveraging Generative AI to develop more efficient, scalable, and secure web-based solutions. As the digital landscape continues to evolve, Generative AI for Web Engineering Models is a valuable resource for those seeking to stay at the forefront of web technology.
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R 428.00
R 764.00
R 977.00
R 519.00
R 382.00
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R 458.00
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R 539.00
R 489.00
Enterprise Guide for Implementing Generative AI and Agentic AI
Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities.This book begins by guiding you through the technological evolution of AI, from early machine learning to today's large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action.The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will: Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.Learn how to develop and implement a production-ready Agentic AI use case.Discover best practices for building scalable, secure, and enterprise-grade AI solutions.Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices. This book is for: Enterprise Software Engineers and Architects
R 912.00
R 1,069.00
R 397.00
Understanding the Ethics of Generative AI
The book discusses the ethical complexities that software developers face as they build AI systems capable of autonomous creation. It explores the ethical decisions developers must make when building generative AI systems, from mitigating bias in training data to protecting user privacy and navigating regulatory compliance. Through real-world case studies and actionable frameworks, it equips technical professionals with both the understanding and tools to build AI systems that are fair, transparent, and worthy of public trust.Identifies and corrects algorithmic bias in training datasets, ensuring AI systems produce equitable outputs that don't systematize discriminationDesigns privacy-first AI architectures and implements transparency practices that comply with data protection regulations while building user trustNavigates evolving legal and regulatory landscapes (GDPR, AI Act, sector-specific rules), helping teams stay ahead of compliance requirementsApplies ethical frameworks to real-world decisions: what to do when fairness and accuracy conflict, how to audit AI systems for hidden harms, when to say no to a projectProvides a governance model for embedding ethics into development workflows, not as an afterthought but as core design practice
R 5,165.00
R 428.00
R 3,126.00
Integration Strategies of Generative AI in Higher Education
Amidst the rapid evolution of educational technology, a pressing challenge confronts higher education institutions: how to effectively integrate generative artificial intelligence (AI) into their existing frameworks. As universities strive to adapt to the digital age, they are met with the complexities of incorporating AI-driven solutions to enhance teaching, learning, and administrative processes. However, the lack of comprehensive strategies and guidance hinders their ability to leverage AI's full potential, leaving educators and administrators grappling with uncertainty. In response to this critical dilemma, Integration Strategies of Generative AI in Higher Education emerges as a guide for clarity and innovation. By offering methodological insights and practical frameworks, this book equips higher education stakeholders with the tools needed to navigate the intricacies of AI integration. From curriculum enhancement to AI-driven content creation, the book provides actionable strategies tailored to the unique needs and challenges of higher education institutions. As educators, administrators, and policymakers seek to harness the transformative power of AI in education, this book serves as an indispensable roadmap for success. By synthesizing best practices from diverse educational contexts and addressing key considerations such as regulatory frameworks and ethical implications, this book empowers stakeholders to embrace AI with confidence, ushering in a new era of innovation and excellence in higher education.
R 9,485.95
R 1,069.00
R 912.00
R 544.90
R 916.00
