Search results for "Python for Generative AI"
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Generative AI and Creativity
Generative AI and Creativity: From Theory to Practice is an immersive exploration into the revolutionary domain of generative artificial intelligence. It takes a step-by-step journey starting with the fundamentals of generative AI and progressing to advanced applications. The book unravels the complexities of generative models, delving into key techniques such as generative adversarial networks (GANs) and variational autoencoders (VAE)s, providing a solid foundation for readers to understand the underlying principles.FEATURES• Explores the collaboration between humans and AI in creative processes• Examines generative art movements, graphic design applications, and artist-AI collaborations• Defines creativity in AI, by showcasing case studies and discussing ethical considerationsMoving beyond creative content generation in art and music to practical implementations in industries like healthcare and finance, the book showcases the versatility and transformative potential of generative AI across diverse domains. Readers will gain practical skills through coding examples, case studies, and collaborative projects, empowering them to integrate generative AI into their own work.The book is enriched with stories of innovation, featuring pioneers who have leveraged generative AI to push the boundaries of creativity. Ethical considerations and the societal impact of generative AI are also explored to foster a holistic understanding of the technology.
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Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
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Generative AI and Creativity
Generative AI and Creativity: From Theory to Practice is an immersive exploration into the revolutionary domain of generative artificial intelligence. It takes a step-by-step journey starting with the fundamentals of generative AI and progressing to advanced applications. The book unravels the complexities of generative models, delving into key techniques such as generative adversarial networks (GANs) and variational autoencoders (VAE)s, providing a solid foundation for readers to understand the underlying principles.FEATURES• Explores the collaboration between humans and AI in creative processes• Examines generative art movements, graphic design applications, and artist-AI collaborations• Defines creativity in AI, by showcasing case studies and discussing ethical considerationsMoving beyond creative content generation in art and music to practical implementations in industries like healthcare and finance, the book showcases the versatility and transformative potential of generative AI across diverse domains. Readers will gain practical skills through coding examples, case studies, and collaborative projects, empowering them to integrate generative AI into their own work.The book is enriched with stories of innovation, featuring pioneers who have leveraged generative AI to push the boundaries of creativity. Ethical considerations and the societal impact of generative AI are also explored to foster a holistic understanding of the technology.
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Generative AI on Kubernetes
Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.Learn to run GenAI models on Kubernetes for efficient scalabilityGet techniques to train and fine-tune LLMs within Kubernetes environmentsSee how to deploy production-ready AI systems with automation and resource optimizationDiscover how to monitor and scale GenAI applications to handle real-world demandUncover the best tools to operationalize your GenAI workloadsLearn how to run agent-based and AI-driven applications
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Practical Guide to Machine Learning, NLP, and Generative AI: Libraries, Algorithms, and Applications
This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment.The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications.Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks.Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques.Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models.The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series
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ML and Generative AI in the Data Lakehouse
In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform
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Generative AI for Personalized Learning
The rapid evolution of generative AI (GenAI) is reshaping education, making personalized learning more adaptive, accessible, and effective than ever before. This book explores the profound ways in which GenAI-driven tools—such as GPT-4, transformers, and GANs—are transforming traditional teaching and learning paradigms. From real-time curriculum adaptation to AI-powered tutors, GenAI is not just enhancing education—it is redefining it.Through in-depth analysis and real-world case studies from Finland, Singapore, Japan, and the United States, this book examines how AI-driven learning environments foster student engagement and improve outcomes. It highlights the shift from one-size-fits-all education to truly individualized learning paths, where AI dynamically adjusts content, pacing, and feedback to each learner’s needs. Teachers, rather than being replaced, are empowered with intelligent tools that enhance their instructional strategies.The rise of AI in education also brings critical challenges. This book does not shy away from discussing ethical concerns, data privacy issues, and the risks of bias in AI-driven education. It provides a balanced perspective, offering strategies to ensure that GenAI serves all students equitably, promoting fairness and inclusivity in the classroom. Whether you are an educator, researcher, policymaker, or technology leader, Generative AI for Personalized Learning offers valuable insights into the future of education. It presents a forward-thinking vision—ne where AI and human expertise work in harmony to create a more responsive, scalable, and learner-centric educational experience.The future of learning is not coming—t is already here. This book is your essential guide to understanding and navigating this transformation.
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Generative AI for Next-Gen Defence Strategies
This book aims to explore the manifold role of Generative AI in enhancing defence capabilities and its potential to transform future warfare. It is divided into three sections. The first discusses the fundamentals of Generative AI and its strategic significance in modern defence sector. This section sets the foundation for understanding how these technologies are shaping current defence strategies and providing a competitive edge. The second explores the application of Generative AI across military, aerospace and naval domain. This section starts by exploring the use of Generative AI for Strategic and Operational Planning, Battlefield Simulation and Training, and development of Autonomous Vehicles and Exoskeletons. The section also covers the use of Generative AI in Drone Swarms, Autonomous Navigation, and Mission Control, as well as its role in maintaining and monitoring Aerospace Infrastructure. Advanced applications such as Radar Systems, Underwater Drones, and Naval Robotics are also discussed in the section. Additionally, the predictive capabilities of Generative AI, including Trajectory Prediction and Failure Prediction for ships and aircraft, and its significance in Naval Warfare and Underwater Sensing have been explored. It also examines the critical role of Generative AI in Cyber Security and Network Defence. The last section of the book addresses the challenges of Generative AI in Defence and the ethical considerations that need to be addressed while using Generative AI in Defence sector. This section also critically assesses the potential threats posed by Generative AI in Cognitive Warfare, highlighting the need for robust ethical frameworks and policies to mitigate risks.The book will be useful for defence professionals, researchers, and technologists who want to understand the transformative potential of Generative AI in defence and attempts to inspire innovative thinking and foster a deeper understanding of the strategic value of Generative AI.
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Learning Generative AI Tools for Excel
Looking to take your Excel skills to the next level? With the power of AI, you can supercharge your workflows, automate repetitive tasks, and perform advanced data analysis. Written for Excel users with little to no AI experience, this book bridges the gap between traditional spreadsheet skills and the revolutionary capabilities of generative AI--empowering professionals to work smarter, faster, and with greater precision. Whether you're a data analyst, financial professional, project manager, or Excel enthusiast, this book will give you practical tutorials and actionable insights for integrating AI into your everyday work. Learn how to prepare, analyze, and visualize data using tools like ChatGPT and Copilot, and leverage AI's power to generate insights that drive better business decisions. With its step-by-step guidance, this book will be your go-to reference as you unlock a new level of productivity and analytical expertise. Learn to integrate ChatGPT and Copilot with Excel for enhanced efficiency Understand AI-driven techniques for data preparation, analysis, and visualization in Excel Discover the landscape of AI add-ins like OpenAI API and how to use them effectively Automate routine Excel tasks using generative AI for faster, more precise results Analyze data with AI tools to uncover actionable insights and drive smarter decisions
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Generative AI and LLMs
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
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Generative AI in Higher Education:Guiding Principles for Teaching and Learning
Divided into two volumes, this book develops guiding principles for higher education institutions to use GenAI effectively and ethically in teaching and learning, articulating a roadmap for implementation at institutional levels and addressing the conundrum of using GenAI in higher education. As higher education institutions take different attitudes and approaches to Generative AI (GenAI), with some viewing it as a threat to academic integrity and therefore implementing a policy of banning its use, while others have embraced it as an innovation to academic practice and have implemented guidance on how to use it ethically, this book makes clear that GenAI , such as ChatGPT, is not the problem itself, but rather the issue is how we engage with it.
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Generative AI for Leaders Playbook
Lead the AI revolution with confidence. Learn how to craft strategy, build AI teams, and drive innovation responsibly with this practical leadership playbook for the Generative AI era.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild AI fluency and lead digital transformation with confidenceDesign enterprise-wide AI strategy, teams, and governance modelsUnlock growth with responsible and scalable Generative AI adoptionBook DescriptionGenerative AI has shifted from a technical breakthrough to a leadership imperative. This essential guide helps executives, directors, and tech leads navigate the transformation, bridging AI’s technical realities with enterprise applications and providing the frameworks and vocabulary needed to make informed decisions.Written for strategic leaders and decision-makers, this practical playbook demystifies key concepts, including generative AI, large language models (LLMs), and emerging agentic AI systems. It shows you how to develop AI strategies aligned with business goals, build AI-first organizations, and establish governance frameworks for responsible, compliant adoption. Real-world examples show how leading companies use AI to drive innovation, efficiency, and growth.Beyond strategy, you’ll learn how to build and scale AI teams, evaluate technology vendors, and integrate AI across business functions. You’ll also explore global policy trends, ethical implications, and the governance frameworks emerging across jurisdictions.This playbook provides a roadmap for turning AI disruption into sustainable competitive advantage, equipping executives with the strategic understanding and practical guidance needed to lead, implement, and scale AI responsibly.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand generative and agentic AI capabilities and business valueBuild organizational AI literacy and cultural readinessCraft an enterprise AI strategy aligned with business goalsDesign and scale high-performing AI teams and structuresEvaluate AI platforms and vendors for transparency and reliabilityEmbed responsible AI governance and compliance frameworksWho this book is forThis book is for AI professionals, AI business leaders, founders, and senior managers who need to navigate the strategic, operational, and ethical challenges of AI adoption. It’s also invaluable for consultants, policy leaders, and transformation specialists advising enterprises on AI readiness. No coding knowledge is required—only a vision to lead AI-powered change across industries.
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Communication Skills for Generative AI
Communication Skills for Generative AI: A Practical Guide for Humans is the first comprehensive book to treat communication with generative AI as a distinct human competence—analogous to digital literacy, media literacy, or academic writing skills. Instead of framing the field through the narrow lens of “prompt engineering,” this book introduces a broader, deeper, and more natural paradigm: human–AI communication. It argues that as generative AI becomes embedded in daily life, professional work, research, education, and creative practices, humans must learn not merely to instruct AI systems but to communicate with them effectively, collaboratively, and safely. The book covers the foundations, principles, techniques, and future directions of communicating with modern AI systems. It begins by explaining why the rise of generative AI requires a new set of human capabilities, then outlines the psychological, linguistic, cognitive, and interactional mechanisms that shape human–AI dialogue. It provides a systematic structure of principles (clarity, intent, iteration, cognitive alignment, ethical responsibility) and practical techniques (contextual communication, constraint-based approaches, multimodality, iterative refinement, tone and persona control). The book also examines pitfalls and sources of misunderstanding—such as ambiguity, hallucination, overtrust, and guardrails—and offers strategies for detecting and resolving them. A central contribution of the book is its treatment of collaborative and co-creative communication, where AI is understood not as a passive tool but as an active cognitive partner capable of supporting reasoning, generating ideas, and co-creating text, visuals, and solutions. Another unique perspective is the chapter on metacommunication, which teaches readers how to talk about the communication process itself— expressing preferences, adjusting strategies, and aligning expectations during the dialogue. The book concludes with a forward-looking exploration of the future of AI communication: post-prompting paradigms, persistent personal assistants, intuitive and emotional interfaces, hybrid reasoning models, and the evolving competencies humans will need as AI becomes increasingly autonomous and embedded. This book relates to existing literature on prompt engineering, human–computer interaction, and AI literacy but significantly expands beyond them. While traditional books focus on crafting prompts or operating tools, this work builds a conceptual and methodological foundation for treating AI as a communicative partner. It introduces a unified framework for understanding interaction across text, multimodal interfaces, and emerging real-time systems. This broader perspective positions the book as a pioneering contribution that addresses an urgent gap in current academic, professional, and educational discourse.
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Generative AI on Microsoft Azure
Companies are now moving generative AI projects from the lab to production environments. To support these increasingly sophisticated applications, they're turning to advanced practices such as multiagent architectures and complex code-based frameworks. This practical handbook shows you how to leverage cutting-edge techniques using Microsoft's powerful ecosystem of tools to deploy trustworthy AI systems tailored to your organization's needs. Written for and by AI professionals, Generative AI on Microsoft Azure goes beyond the technical core aspects, examining underlying principles, tools, and practices in depth, from the art of prompt engineering to strategies for fine-tuning models to advanced techniques like retrieval-augmented generation (RAG) and agentic AI. Through real-world case studies and insights from top experts, you'll learn how to harness AI's full potential on Azure, paving the way for groundbreaking solutions and sustainable success in today's AI-driven landscape. Understand the technical foundations of generative AI and how the technology has evolved over the last few yearsImplement advanced GenAI applications using Microsoft services like Azure AI Foundry, Copilot, GitHub Models, Azure Databricks, and Snowflake on AzureLeverage patterns, tools, frameworks, and platforms to customize AI projectsManage, govern, and secure your AI-enabled systems with responsible AI practicesBuild upon expert guidance to avoid common pitfalls, future-proof your applications, and more
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