Search results for "Python for Generative AI"
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Essential Math for AI
Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and moreLearn how to adapt mathematical methods to different applications from completely different fieldsGain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
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Using Generative AI in Historical Practice
'Using Generative AI in Historical Practice' argues that generative models are reshaping historical scholarship. Rejecting medium - and long -term speculation, it focuses on near-term practice: how historians can use AI now to augment their research through context-aware dialogue, semantic search, network visualization, multimodal source analysis, and code-assisted workflows. It details methods for context management, task design, and response structure, while warning against cognitive offloading and model bias. While it offers a variety of novel methodologies, the book insists on the indispensability of human agency and taste. Case studies range from Augustine of Hippo to early cinematography, demonstrating the possibilities and limits of generative AI. It concludes with a call to historians to engage with the technology critically and productively, reimagining AI-assisted scholarship without surrendering disciplinary standards and aims.
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Humanising Language Education in the Generative Artificial Intelligence Age
Examining the presence and influence of GenAI in the contexts of English for Academic Purposes (EAP), and written composition within university-level English as a foreign language (EFL) programmes, this book asserts a humanist view of language education informed by Critical Realism and the social realist philosophy of education.This author looks at the GenAI-language education interaction through explicit philosophical engagement, with the aim of strengthening a critical and emancipatory approach to EAP, written composition, and language education. By highlighting technological determinism and relating it to broader neoliberalist mechanisms, it provides new insights into the social, political, economic, cultural and historical contexts within which the use of GenAI technologies by language teachers and learners can be understood.Humanising Language Education in the Generative Artificial Intelligence Age will appeal to students and scholars of linguistics, education and sociology as well as organizations devoted to understanding the evolution, application, and impact of GenAI on education and language learning.
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Business Analytics and Artificial Intelligence
Business Analytics and Artificial Intelligence provides an in-depth exploration of the intersection of business analytics, machine learning, and artificial intelligence (AI). Tailored for undergraduate, graduate, and professional learners, it equips readers with the knowledge and skills to leverage data-driven insights and AI technologies for strategic decision-making, operational optimization, and innovation in a dynamic global business environment. This book bridges the gap between theory and practice by:- Introducing the foundational principles of business analytics and AI- Demonstrating the integration of advanced AI techniques into business analytics processes - Providing a unified guide to business analytics and machine learning with a focus on real-world applications- Presenting a clear, practical, and accessible framework to harness AI and machine learning for solving complex business problems, optimizing operations, and driving innovation- Exploring the ethical, legal, and social implications of AI in business contexts- Presenting case studies and real-world applications from diverse industries to showcase best practices.The textbook combines foundational concepts, advanced techniques, and hands-on applications, catering to readers with varying levels of technical expertise. It includes global perspectives of BA and AI, case studies, practical examples, and coding snippets (in Python or R) to help readers implement AI-driven solutions in their organizations. Topics span predictive analytics, machine learning, data visualization, natural language processing, and decision support systems, emphasizing how these tools empower businesses to stay competitive and ensure operational sustainability.
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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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Generative Engine Optimization
Optimize your content for AI answer engines and protect your organic traffic As 77% of ChatGPT users now treat it like a search engine, traditional SEO alone no longer secures brand visibility. Generative Engine Optimization (GEO): The Definitive Guide to AI SEO, by Siege Media founder Ross Hudgens, is a practical guide to optimizing content for AI SEO and answer engines including ChatGPT, Google Gemini, Perplexity, and Claude so your brand appears positively in LLM-generated responses. Generative Engine Optimization (GEO) covers the practice of structuring content so it performs well inside LLM-powered systems that generate responses rather than retrieve links. Hudgens draws on experience driving organic growth for clients like Adidas, HubSpot, Instacart, and TripAdvisor to show how SEO practitioners and marketing leaders can modernize their search programs and drive measurable sales through AI SEO tools. Readers will also discover: Actionable strategies for increasing how often AI tools mention your brand positively in generated responsesTechniques for adapting existing SEO and content marketing workflows to account for LLM-driven search behaviorMethods to future-proof your organic growth program as traditional search engine traffic declines across industriesGuidance for marketing leaders on evaluating and implementing GEO practices across content, PR, and affiliate teamsReal-world approaches from an agency that has driven organic growth for blue-chip brands like Airbnb and HubSpot Ideal for SEO practitioners, marketing leaders at technology companies, CMOs, and content marketers, Generative Engine Optimization (GEO) provides the frameworks needed to modernize search programs, secure brand visibility inside AI answer engines, and stay ahead as consumer search behavior shifts toward large language models.
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Empowering the Public Sector with Generative AI
This is your guide book to Generative AI (GenAI) and its application in addressing real-world challenges within the public sector. The book addresses a range of topics from GenAI concepts and strategy to public sector use cases, architecture patterns, and implementation best practices. With a general background in technology and the public sector, you will be able to understand the concepts in this book.The book will help you develop a deeper understanding of GenAI and learn how GenAI differs from traditional AI. You will explore best practices such as prompt engineering, and fine-tuning, and architectural patterns such as Retrieval Augmented Generation (RAG). And you will discover specific nuances, considerations, and strategies for implementation in a public sector organization. You will understand how to apply these concepts in a public sector setting and address industry-specific challenges and problems by studying a variety of use cases included in the book in the areas of content generation, chatbots, summarization, and program management. What You Will LearnGenAI concepts and how GenAI differs from traditional AI/ML Prompt engineering, fine-tuning, RAG, and customizing foundation modelsStrategy, methodologies, and frameworks for the public sectorPublic sector use cases in the areas of content generation, summarization, and chatbots, plus program management, analytics, business intelligence, and reportingArchitecture and design patterns Implementation, operations, and maintenance of GenAI applications Who This Book Is ForTechnology and business leaders in the public sector who are new to AI/ML and are keen on exploring and harnessing the potential of Generative AI in their respective organizations.
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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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Generative AI for Cybersecurity
This book lays a systematic foundation for professionals, researchers, and industry readers who are interested the applications and implications of generative AI for cybersecurity. It covers the latest advances in generative AI and its applications, risks, and opportunities in cybersecurity.The authors first introduce the fundamental background of generative AI, the latest cybersecurity issues, and related potential applications in cybersecurity systems. Following this, they comprehensively review the state-of-the-art research and development, covering various aspects of generative AI applications in this area and related challenges and issues, such as training data availability, computational complexity, generalization to different scenarios, AI governance, quantum-empowered AI and many more. These discussions provide a strong understanding of recent advances in the two fields of generative AI and cybersecurity and the convergence of these domains, which will help readers to shape the field as it matures. Hands-on experiments presented throughout will also give them the practical skills for success. By leveraging its capabilities, readers can overcome challenges, understand the risks, enhance performance, and unlock new opportunities for handling the challenges of cybersecurity with generative AI. Consequently, they will be able to apply their knowledge to utilize generative AI in cybersecurity applications to prevent economic and other losses due to cyber risks such as phishing, fake news, deepfake-based fraud, and other cyberattacks.The contents of this book are appropriate for a wide range of readers from general readers to industry experts and scientists. Because it bridges the gap between generative AI and cybersecurity, experts from both fields will benefit from the information presented within. Students with a background in either area will also benefit from the approach that leads from general to specific applications.
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Generative AI in R
Leverage Generative AI within the R programming environment and prepare for future directions and how new innovations can be applied in the R ecosystem. This pioneering book is designed to bridge the gap between the advanced realms of Generative AI and the practical, statistical computing power of R. You'll begin with an introduction to Generative AI principles and its significance in the current data-driven landscape. You'll then dive into the practicalities of implementing generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in R. See how R, most known for its statistical analysis, can also be used for creative synthetic data, improving model robustness, and generating innovative insights from data. Additionally, this book addresses the demand for ethical AI by emphasizing the use of synthetic data to tackle privacy and data scarcity issues--concerns particularly relevant in healthcare, finance, and social research. We are at a pivotal moment in the evolution of AI and data science. With AI's growing importance, the book's focus on R makes advanced techniques more accessible, promoting ethical and innovative data science practice, preparing readers for upcoming trends. What You Will Learn Grasp the core concepts of Generative AI and its significance in the broader AI landscape. Implement various generative models in R, such as GANS and VAEs. Generate high-quality synthetic data. Apply advanced techniques for improving efficiency and effectiveness of models for different applications. Understand Gen AI ethical considerations. Who This Book Is For Data scientists and statisticians with intermediate R programming skills who want to expand into Generative AI for data analysis and problem-solving. AI enthusiasts and data analysts looking to apply Generative AI techniques in R to enhance their analytical capabilities.
R 912.00
Generative AI in Higher Education
Chan and Colloton’s book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.Since November 2022, every conversation in higher education has involved ChatGPT and its impact on all aspects of teaching and learning. The book explores the necessity of AI literacy tailored to professional contexts, assess the strengths and weaknesses of incorporating ChatGPT in curriculum design, and delve into the transformation of assessment methods in the GenAI era. The authors introduce the Six Assessment Redesign Pivotal Strategies (SARPS) and an AI Assessment Integration Framework, encouraging a learner-centric assessment model. The necessity for well-crafted AI educational policies is explored, as well as a blueprint for policy formulation in academic institutions. Technical enthusiasts are catered to with a deep dive into the mechanics behind GenAI, from the history of neural networks to the latest advances and applications of GenAI technologies.With an eye on the future of AI in education, this book will appeal to educators, students and scholars interested in the wider societal implications and the transformative role of GenAI in pedagogy and research.
R 1,942.00
AI-Assisted Programming for Web and Machine Learning
Speed up your development processes and improve your productivity by writing practical and relevant prompts to build web applications and Machine Learning (ML) modelsPurchase of the print or Kindle book includes a free PDF copyKey FeaturesUtilize prompts to enhance frontend and backend web developmentDevelop prompt strategies to build robust machine learning modelsUse GitHub Copilot for data exploration, maintaining existing code bases, and augmenting ML models into web applicationsBook DescriptionAI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks.Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.What you will learnSpeed up your coding and machine learning workflows with GitHub Copilot and ChatGPTUse an AI-assisted approach across the development lifecycle Implement prompt engineering techniques in the data science lifecycleDevelop the frontend and backend of a web application with AI assistance Build machine learning models with GitHub Copilot and ChatGPT Refactor code and fix faults for better efficiency and readability Improve your codebase with rich documentation and enhanced workflows Who this book is forExperienced developers new to GitHub Copilot and ChatGPT can discover the best strategies to improve productivity and deliver projects quicker than traditional methods. This book is ideal for software engineers working on web or machine learning projects. It is also a useful resource for web developers, data scientists, and analysts who want to improve their efficiency with the help of prompting. This book does not teach web development or how different machine learning models work.
R 1,628.00
Using Generative AI for SEO
Generative AI has brought artificial intelligence into the mainstream. The natural language capabilities of this technology provide digital marketers with exciting new ways to use AI in their day-to-day operations. Yet few SEO practitioners understand how to integrate this capability into their business.Using Generative AI and SEO is the first authoritative book dedicated to helping SEO practitioners effectively integrate generative AI capabilities into their work. No matter where you are in your SEO journey, it's critical to get started so that your organization can gain a competitive advantage. This book provides the necessary background, knowledge, skills, and practical examples to get you working quickly and confidently with this essential new tool.Understand how generative AI can help you impact SEOExplore practical applications you can use right nowLearn the competencies you'll need to ensure your toolkit remains relevantImprove content creation throughput while improving qualityConsider risks and limitations of AI applications in SEOBuild a generative AI capability within your organization
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Generative AI in Teaching and Learning
Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes. The book covers a wide range of topics, including generative AI applications in artwork, coding, writing, research, collaboration, and more. It examines the implications of public generative AI interfaces and explores how generative AI can be effectively utilized in formal education settings, such as accredited institutions. The roles of teachers, content creation, instructional design, research, community service, sports, policies, assignments, grading, student work, and programming are all examined in relation to generative AI. Furthermore, the book explores the use of generative AI in nonformal and informal teaching and learning, emphasizing the opportunities for self-learning, group learning, and social change that arise from its implementation. It also delves into the professional ethics surrounding the application of generative AI in education. While examining the potential benefits of generative AI, the book also addresses the risks associated with demotivation for skill acquisition, deskilling, and downskilling. It highlights the opportunities for upskilling and encourages educators and learners to engage in learning practices that leverage the power of generative AI.
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