Search results for "python for generative ai"
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Python All-in-One For Dummies
Everything you need to know to get into Python coding, with 7 books in one Python All-in-One For Dummies is your one-stop source for answers to all your Python questions. From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. This book is great as a starting point for those new to coding, and it also makes a perfect reference for experienced coders looking for more than the basics. Apply your Python skills to data analysis, learn to write AI-assisted code using GitHub CoPilot, and discover many more exciting uses for this top programming language. Get started coding in Python—even if you’re new to computer programmingReference all the essentials and the latest updates, so your code is air-tightLearn how Python can be a solution for large-scale projects and big datasetsAccelerate your career path with this comprehensive guide to learning PythonExperienced and would-be coders alike will love this easy-to-follow guide to learning and applying Python.
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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.
R 6,680.18
Generative AI
The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of Phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks.The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for Phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website.But, Generative AI can also be used for the good also, to combat Phishing Attacks. This is the topic of this book. In this, we cover the following:A review of PhishingA review of AI, Neural Networks, and Machine LearningA review of Natural Language Processing, Generative AI, and the Digital PersonA proposed solution as to how Generative AI can combat Phishing Attacks, as they relate to Privileged Access accounts
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Introduction to Large Language Models
The book offers an introduction to Large Language Models that bridge foundational natural language processing (NLP) concepts with the advanced techniques underlying large language models (LLMs). It offers a structured exploration of NLP evolution, from rule-based approaches to transformer architectures. Covering key principles such as tokenisation, attention mechanisms, and model architectures (BERT, GPT, T5), the book explains pretraining objectives like masked and causal language modeling. It also addresses optimisation techniques such as LoRA, pruning, and quantisation for efficient LLM deployment. Multi-modal models, including GPT-4 and PaLM-E, are explored alongside retrieval-augmented generation and AI-powered agents.Discusses foundational NLP concepts, theoretical depth, advanced techniques, and real-world applications.Covers perplexity, BLEU, ROUGE, and datasets like SuperGLUE and SQuAD for assessing LLM performance, discusses LoRA, pruning, and quantisation to optimise LLM deployment in resource-constrained settings.Explores GPT-4, PaLM-E, and retrieval-augmented generation, expanding beyond traditional NLP models.Provides Python implementations for fine-tuning, classification, summarisation, and conversational AI tasks.Highlights use cases in text generation, code generation, sentiment analysis, and multimodal AI.This book is an invaluable textbook for students, researchers, and industry professionals seeking a deep technical understanding of LLMs and their applications.
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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 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.
R 6,118.00
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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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 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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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 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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AI Agents with MCP
Since its release in late 2024, Anthropic's Model Context Protocol (MCP) has redefined how developers build and connect AI agents to tools, data, and each other. AI Agents with MCP is the first comprehensive guide to this rapidly emerging standard, helping engineers unlock its full potential with hands-on projects. Whether you're developing agentic workflows, bridging tools across platforms, or creating robust multiagent systems, this book walks you through every layer of MCP--from protocol structure to server and client implementation. Author Kyle Stratis provides the practical expertise needed to build fully functional MCP servers, clients, and more. Unlike high-level overviews or fragmented documentation, this book gives you a deep systems-level understanding of MCP's capabilities--and limitations. With its flexible, model-agnostic design, MCP continues to gain traction across the generative AI community; this book ensures you're ready to build with it confidently and effectively. Understand the structure and core concepts of the Model Context Protocol Build complete MCP servers, clients, and transport layers in Python Consume tools, prompts, and data via MCP-based agent workflows Extend agent capabilities with MCP for large-scale and AI-native systems
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