Search results for "Deep Generative Modeling"
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Retrieval Augmented Generation In Production: Architecture, Patterns, And Runbooks
This book is a practical, end-to-end guide to building product-implementation-ready Retrieval-Augmented Generation (RAG) systems for high-stakes domains such as healthcare, finance, education, legal services, and customer support. While Artificial Intelligence (AI) advances rapidly, Large Language Models (LLMs) continue to face challenges with factual consistency and domain-specific accuracy. LLM-based RAG systems address these limitations by integrating live, external knowledge sources to produce grounded, current, and trustworthy outputs.Readers are taken through the full RAG pipeline with MLOps/LLMOps — while tackling 30+ real-world implementation challenges, including data parsing & chunking, prompt rephrasing, retrieval quality, response synthesis, hallucination mitigation, evaluation frameworks, serving & monitoring enhancement, orchestration optimization, and graph-, tabular-, and agentic-RAG patterns. Clear architectures, case studies, and runnable code illustrate how to design, implement, validate, monitor, and scale robust RAG systems.The book also provides a balanced perspective on the current limitations of RAG approaches and their future potential as part of emerging agentic AI ecosystems. Whether you are an engineer, product leader, or researcher, this book equips you to deliver reliable, business-ready AI solutions while staying ahead of rapidly evolving technologies.All supplementary material (i.e. sample codes) are available at https://github.com/junxu-ai/RAGCodes.
R 2,491.00
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
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Building Generative AI Solutions for Industry with AWS
Building Generative AI Solutions for Industry with AWS is a comprehensive guide for developers, and data scientists aiming to harness the power of Generative AI across a range of industrial domains using Amazon Web Services. This hands-on book provides readers with the knowledge and practical skills to design, secure, govern, and deploy GenAI solutions effectively in real-world environments. The book begins with an introduction to the fundamentals of Generative AI and the AI/ML services offered by AWS. Readers will explore industry use cases and learn how to kick-start their journey building GenAI applications on the AWS cloud. It focuses on security and governance, covering essential practices for data privacy, access control, encryption, compliance, and ethical AI deployment. Each chapter then dives deep into specific industry applications--automotive, manufacturing, healthcare, financial services, and media & entertainment. From autonomous driving and predictive maintenance to fraud detection and content creation, readers will learn how GenAI is transforming operations and unlocking new opportunities in these fields. The book also addresses cost management strategies for GenAI on AWS, offering guidance on optimizing resources and budgeting efficiently. Concluding with future possibilities and implementation best practices, this book equips professionals with the tools and insights to build scalable, secure, and compliant GenAI systems tailored to their domain. What you will learn: Understand the fundamentals of Generative AI and AWS AI/ML services.How to build and deploy secure, compliant GenAI applications on AWS.Explore real-life GenAI use cases across industries like automotive, healthcare, and finance.Gain insights into managing GenAI costs and optimizing AWS resources. Who this book is for: Software developers, machine learning engineers, data scientists, and cloud architects who want to build real-life Generative AI solutions using AWS.
R 2,417.79
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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Towards a General Theory of Deep Downturns
Joseph Stiglitz examines the theory behind the economic downturns that have plagued our world in recent times. This fascinating three-part lecture acknowledges the failure of economic models to successfully predict the 2008 crisis and explores alternative models which, if adopted, could potentially restore a stable and prosperous economy.
R 1,578.61
AI and Precision Medicine in Infectious Disease Management
This book presents technical advancements of artificial intelligence (AI)–based technologies and computational biology approaches, highlighting their applications in developing smart healthcare and precision medicine for managing infectious diseases. It discusses cutting-edge AI tools, particularly machine and deep learning models, and generative AI to assist in disease prediction and prevention, disease surveillance and diagnosis, drug discovery, drug design and development, healthcare, personalized medicine, pharmacology, and vaccine development. The contents fit the goal of establishing "Good Health and Well-Being," which is one of the "Sustainable Development Goals (SDGs) 2030."Key Features:• Includes AI/omics approaches in precision medicine and smart healthcare• Summarizes AI-driven drug and vaccine developments and AI tools for infectious disease prevention for clinicians, researchers, and policymakers• Proposes AI-based strategies for disease prediction, surveillance, and diagnosis
R 2,821.00
Building Generative AI Services with Fastapi
Ready to build production-grade applications with generative AI? This practical guide takes you through designing and deploying AI services using the FastAPI web framework. Learn how to integrate models that process text, images, audio, and video while seamlessly interacting with databases, filesystems, websites, and APIs. Whether you're a web developer, data scientist, or DevOps engineer, this book equips you with the tools to build scalable, real-time AI applications.Author Alireza Parandeh provides clear explanations and hands-on examples covering authentication, concurrency, caching, and retrieval-augmented generation (RAG) with vector databases. You'll also explore best practices for testing AI outputs, optimizing performance, and securing microservices. With containerized deployment using Docker, you'll be ready to launch AI-powered applications confidently in the cloud.Build generative AI services that interact with databases, filesystems, websites, and APIsManage concurrency in AI workloads and handle long-running tasksStream AI-generated outputs in real time via WebSocket and server-sent eventsSecure services with authentication, content filtering, throttling, and rate limitingOptimize AI performance with caching, batch processing, and fine-tuning techniques
R 1,596.00
Hands-On Time Series Analysis with R
Master modern time series forecasting in R using real datasets, applied models like ARIMA and Prophet, and advanced tools like LSTM for business-ready analysisKey FeaturesBuild a practical skills in time series forecasting using R from scratchLevereage traditional and deep learning models including ARIMA, Prophet, and LSTMAnalyze and visualize real-world datasets to drive decisions and discover insightsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionHands-on Time Series Analysis with R, Second Edition, is a comprehensive, practical guide to understanding and applying time series analysis using R. Designed for professionals and students alike, the book will help you dissect time-ordered data, uncover trends, detect anomalies, and forecast future events. You’ll start with R fundamentals and data structures, progressing through time series visualization and decomposition,before covering classical forecasting methods like exponential smoothing and ARIMA. You’ll also familiarize yourself with cutting-edge forecasting techniques such as Bayesian modeling, TBATS, and multivariate forecasting. Building upon the extraordinary success of the first edition, this new edition explores the powerful Prophet library, neural networks, and LSTM models to handle complex forecasting challenges. Unlike other similar books, it uses real-world case studies from domains like e-commerce and supply chain make the content highly applicable, while self-assessment questions and step-by-step tutorials reinforce key concepts. By the end of this book, you’ll be able to confidently apply a wide array of forecasting methods to solve real-world business and research problems.What you will learnWork with time series data to uncover underlying patterns and trendsImplement forecasting models like ARIMA and Prophet to predict future valuesCreate powerful data visualizations to communicate time series insightsLeverage R for time-based analysis across various domainsBuild and fine-tune models for different data scenariosDetect anomalies and adjust models for improved accuracyUse deep learning techniques like LSTM for complex time series tasksWho this book is forThis book is for junior to mid-level professionals in business intelligence, finance, and supply chain roles, as well as academics and students interested in data science. No prior expertise in time series analysis is required, but a working knowledge of basic R programming is helpful.
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Procedural Content Generation via Machine Learning
This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content. The authors survey current and future approaches to generating video game content and illustrate the major impact that PCGML has had on video games industry. In order to provide the most up-to-date information, this new edition incorporates the last two years of research and advancements in this rapidly developing area. The book guides readers on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis. The authors discuss the practical and ethical considerations for PCGML projects and demonstrate how to avoid the common pitfalls. This second edition also introduces a new chapter on Generative AI, which covers the benefits, risks, and methods for applying pre-trained transformers to PCG problems.
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Data-Driven Evolutionary Modeling in Materials Technology
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc.Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms.This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
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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.
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Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things
We are living in an AI and data-driven era. A huge volume of data is generated from all sectors including smart cities, intelligent transportation systems, smart healthcare, education, smart agriculture and smart industrial processes. This information comprises multiple modalities such as textual data, images, sound, gestures and genetic sequences, which are generated through various sources. The internet of multimedia things (IoMT) is one of the key contributors to the collection, analysis and management of voluminous multimodal data. The dominating features of IoMTs are well-timed delivery of data and dependability, but they require high levels of memory and computational power, which requires higher bandwidth and more power. Therefore, they require rigorous quality of service (QoS) and efficient, reliable and secure network frameworks. The objective of this book is to explore machine learning (ML) and deep learning (DL) techniques for securing data with multiple modalities in a wide range of data-centric smart applications in the IoMT framework. In all data-centric application domains where on-time availability of data and reliability are the major concerns, IoMT is contributing significantly. The authors present the challenges faced to organize and process multimodal data in data-centric applications, several types of data modalities, and innovative IoMT frameworks. A particular focus is placed on the role of ML and DL techniques in securing multi-modal data for real-time monitoring in smart environment applications. Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things caters to the needs of AI, big data and IoT advanced students, academic and industry researchers, engineers, security experts, data analysts, and AI and data-centric application developers.
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Concept Drift in Large Language Models
This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence.Examines concept drift in AI, particularly its impact on large language modelsAnalyses how concept drift affects large language models and its theoretical and practical consequencesCovers detection methods and practical implementation challenges in language modelsShowcases examples of concept drift in GPT models and lessons learnt from their performanceIdentifies future research avenues and recommendations for practitioners tackling concept drift in large language models
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Machine Learning and Generative AI in Smart Healthcare
The healthcare landscape is constantly evolving, and one of the most significant concerns that healthcare professionals deal with is understanding how to use biomedical intelligence to improve patient outcomes. With the increasing complexity of healthcare computing systems, including technologies like deep learning and the Internet of Things, it can be challenging to navigate these advancements. Machine Learning and Generative AI in Smart Healthcare is a practical tool for healthcare professionals, researchers, and policymakers who are seeking to implement biomedical intelligence solutions. It provides a clear roadmap for using prescriptive and predictive analytics in machine learning to enhance healthcare outcomes. Going beyond the basics, it delves into healthcare computing and networking complexities. By delving into topics such as data mining, disease prediction, and AI applications, deep learning approaches, decision support systems, and optimization techniques, this book equips readers with the practical knowledge they need to optimize healthcare delivery and management.
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Probabilistic Data-Driven Modeling
This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools useful to individuate regularities, discover patterns and laws in complex datasets, and demonstrates how to apply them to devise models that help to understand these systems and predict their behaviors. By focusing on the estimation of multivariate probabilities, the book shows that the entire domain, from linear regressions to deep learning neural networks, can be formulated in probabilistic terms. This book provides the right balance between accessibility and mathematical rigor for applied data science or operations research students, graduate students in CSE, and machine learning and uncertainty quantification researchers who use statistics in their field. Background in probability theory and undergraduate mathematics is assumed.
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Introduction to General Relativity and Cosmology
Experts Plebański and Krasiński provide a thorough introduction to the tools of general relativity and relativistic cosmology. Assuming familiarity with advanced calculus, classical mechanics, electrodynamics and special relativity, the text begins with a short course on differential geometry, taking a unique top-down approach. Starting with general manifolds on which only tensors are defined, the covariant derivative and affine connection are introduced before moving on to geodesics and curvature. Only then is the metric tensor and the (pseudo)-Riemannian geometry introduced, specialising the general results to this case. The main text describes relativity as a physical theory, with applications to astrophysics and cosmology. It takes the reader beyond traditional courses on relativity through in-depth descriptions of inhomogeneous cosmological models and the Kerr metric. Emphasis is given to complete and clear derivations of the results, enabling readers to access research articles published in relativity journals.
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Learning with Generative Artificial Intelligence
This book delves into the core of education’s digital transformation, presenting a thorough and empirical examination of generative artificial intelligence (GenAI)’s impact beyond the theoretical and fragmented insights prevalent in current discourse.Drawing from peer-reviewed and extensive empirical studies, the contributors aim to unveil the multifaceted effects of GenAI (particularly ChatGPT) on learning. They navigate through topics of interaction, assessment, emotion, effect and efficiency, meta-cognition, and ethics, offering a comprehensive exploration of GenAI’s educational implications. This book presents a closed loop of learning theory, multimodal data, and learning analytics technology. Furthermore, this book builds and proposes core conceptual models for future learning and identifies potential research directions.This book will serve as a foundational reference for educators seeking innovative learning and teaching methods and for researchers and technologists who seek to push the boundaries of educational technology and related areas.
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Multimodal Generative AI in the Enterprise: From Pixels to Profit
Build and deploy enterprise-grade multimodal AI systems using generative models for text, image, audio, and video.Key FeaturesDesign end-to-end multimodal AI systems for enterprise workloadsBuild scalable pipelines using Gemini, Veo, Imagen, Airflow, and KubeflowImplement governance, safety, and compliance for AI-generated contentPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe Multimodal AI Playbook equips enterprise developers and ML engineers with the practical skills needed to build real-world generative systems using text, image, audio, and video models. This book offers a complete stack approach—starting from theory and progressing through tooling, orchestration, governance, and full-scale deployment.Beginning with the foundations of multimodal learning and generative AI models, you’ll explore how to generate modality-specific content while navigating key trade-offs in model selection, scalability, and orchestration. With a dedicated focus on enterprise use cases like automated marketing campaigns, the book shows how to manage model outputs responsibly covering bias detection, IP risk, and content safety.You'll implement a full pipeline using enterprise-ready models such as Gemini, Veo, and Imagen. With forward-looking chapters on real-time generation, 3D assets, and multimodal agents, this book bridges the gap between technical mastery and business impact- preparing you to lead in the AI-first future. What you will learnDesign full-stack multimodal AI architecturesGenerate text, image, audio, and video contentBuild automated AI-powered marketing workflowsCompare and fine-tune leading multimodal modelsDeploy AI pipelines with Airflow and KubeflowImplement AI safety, governance, and complianceCreate real-time and 3D multimodal experiencesIntegrate AI with enterprise cloud platformsWho this book is forSoftware engineers, ML engineers, data scientists, technical product managers, and solution architects looking to build and deploy multimodal generative AI systems in production. Professionals familiar with Python, machine learning concepts, and cloud platforms such as AWS, Azure, or GCP will gain practical guidance for implementing scalable, enterprise-ready AI solutions.
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Generative KI für Dummies
Die Möglichkeiten generativer KI optimal nutzen Mithilfe von generativer Künstlicher Intelligenz lassen sich Texte, Bilder, Videos, Sprache, Musik, Softwarecodes und auch Kunst und Design erschaffen. Doch wie können Sie die Möglichkeiten generativer KI individuell am besten nutzen? KI-Expertin Pam Baker gibt Ihnen einen guten Überblick und führt Sie in die bekanntesten Tools ein. Das Buch erklärt Ihnen, wie Sie geeignete Plattformen finden, gute Prompts schreiben, KI in Ihren Workflow einbinden und gezielt einsetzen – und entscheiden, wann doch ein wenig Human Touch gebraucht wird. Gut verständlich und praxisnah: der ideale Einstieg in ein Thema, das unsere Arbeitswelt rasant verändert. Sie erfahren Welche Plattformen sich für welche Zwecke eignenWie Sie KI-generierte Texte, Bilder, Videos und Audios erstellenWie Sie KI in Ihren Workflow integrierenWie es Ihnen gelingt, KI-Inhalte »menschlicher« zu machen
R 434.00
Introduction to Generative Drug Discovery
This book focuses on the latest advances in computational de novo drug discovery methods, also known as generative drug discovery. This book describes the state-of-the-art methods and applications for de novo design of drug candidates using generative chemistry models as well as ethical aspects of this technology.
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Generative AI for Software Development
As the field of Artificial Intelligence continues to evolve, Generative AI has emerged as a powerful tool in the realm of software development. AI models capable of generating high-quality, human-like code can significantly accelerate the software development process, assist in troubleshooting, and even automate tasks traditionally performed by human developers. Generative AI models, such as OpenAI, DeepMind, and others, are already demonstrating the ability to write functional code, generate documentation, and assist in code review. These models are not only reducing the time it takes to develop applications but are also making software development more accessible to a broader audience, including non-technical users. This book is intended to serve as a comprehensive guide for developers, technology leaders, and anyone interested in understanding how to leverage Generative AI in software development. It will cover the underlying technology behind these models, practical use cases, integration into development workflows, and ethical considerations. Additionally, it explores how the democratization of coding through AI will shape the future of software engineering. By providing insights into real-world applications and best practices for integrating generative AI into the software lifecycle, this book empowers readers to stay ahead of the technological curve in a rapidly evolving industry.
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Combating Misinformation in the Age of Generative AI
The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and therefore hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. In this book, the authors focus on the positive potential of Generative AI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics:• An overview of Generative AI and its relationship with fake news• How Generative AI can be utilized to fight the spread of fake news• Ensuring that the news we encounter on digital media platforms are not misleading or false• Methods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibility• Practical insights into technological advancements and solutions related to the detection and analysis of fake news.Finally, this book serves as a platform for the community to share innovative research on the use of Generative AI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how Generative AI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.
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Making Art With Generative AI Tools
In the dynamic realm of generative artificial Intelligence (AI), the fusion of human creativity and machine intelligence has created a vibrant ecosystem of collaborative artmaking. However, this transformative process brings forth a myriad of concerns, ranging from ethical considerations and the need for originality to navigating the legal complexities surrounding intellectual property. As more and more online communities appear around the use of AI to aid in the creation of images, there arises a pressing need for a comprehensive guide that not only dissects the intricacies of artmaking with generative AI tools but also offers practical solutions to the evolving dilemmas faced by artists, researchers, and technologists. Making Art With Generative AI Tools emerges as an exploration of the challenges posed by this intersection of human expression and artificial intelligence. Artists engaging with generative AI find themselves grappling with issues of authenticity, social toxicity, and the commercial viability of their creations. From avoiding stereotypical visuals to ensuring proper crediting, the realm of generative AI is rife with these complexities. Furthermore, the blurred lines between human and machine authorship necessitate a deeper exploration of how these innovative tools impact creativity, representation, and the very fabric of the art world. Tailored for a diverse audience of researchers, artists, and instructional designers, the book navigates through the intricacies of large language models, provides case studies on the usage of generative AIs, and offers practical guidance on creating visuals for storytelling, teaching, and learning. With a keen focus on fostering human creativity, promoting ethical practices, and addressing the legal nuances of this evolving landscape, the book serves as an indispensable compass for those navigating the uncharted waters of artmaking with generative AI tools.
R 11,193.98
Dynamic Stochastic General Equilibrium Models
This textbook guides the student step-by-step in developing and solving a DSGE (Dynamic Stochastic General Equilibrium) model–not only from the technical and conceptual aspects but also through the simulation process of each model. Characterized by a learning-by-doing approach, the book is set apart from the extant textbooks in three ways. First, it performs all the algebra associated with each model, such as the calculation of steady-state and the log-linearization of the model. Second, each model developed has been generated in Dynare, and every chapter is accompanied by a set of codes (mod-files and m-files) that the reader can use to replicate the model developed in every chapter. Finally, the models considered are toy models in the closed and open economy, which allows the student to learn the basic lessons and understand the fundamental relationships of the variables. All of this prepares the student to deal with more complex models. This book is intended for advanced undergraduate or beginning graduate courses in economics, finance, or applied mathematics, as well as practitioners in central banks that use these models daily in the preparation of forecasts or simulations of aggregate variables.
R 4,396.00
Building Neo4j-Powered Applications with LLMs
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilitiesKey FeaturesDesign vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4jApply best practices for graph exploration, modeling, reasoning, and performance optimizationBuild and consume Neo4j knowledge graphs and deploy your GenAI apps to Google CloudPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEmbark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j.As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI’s most persistent challenges—mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses.Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you’ll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud.By the end of this book, you’ll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.What you will learnDesign, populate, and integrate a Neo4j knowledge graph with RAGModel data for knowledge graphsIntegrate AI-powered search to enhance knowledge explorationMaintain and monitor your AI search application with HaystackUse LangChain4j and Spring AI for recommendations and personalizationSeamlessly deploy your applications to Google Cloud PlatformWho this book is forThis LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.
R 1,465.00
Utilizing Generative AI for Cyber Defense Strategies
As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.
R 12,374.29
Generative AI with LangChain
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applicationsKey FeaturesBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learnDesign and implement multi-agent systems using LangGraphImplement testing strategies that identify issues before deploymentDeploy observability and monitoring solutions for production environmentsBuild agentic RAG systems with re-ranking capabilitiesArchitect scalable, production-ready AI agents using LangGraph and MCPWork with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-miniDesign secure, compliant AI systems aligned with modern ethical practicesWho this book is forThis book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
R 1,954.00
Responsible Implementations of Generative AI for Multidisciplinary Use
Generative artificial intelligence (GAI) represents a profound leap in technological advancement, empowering machines to create content that closely mimics human creativity in various forms. As this technology continues to evolve and permeate multiple industries, it is essential to address the accompanying ethical considerations that arise from its use. Furthermore, there is a need for transparency in how GAI systems are developed and deployed to ensure that they are used responsibly and that their outputs are reliable and fair. Balancing innovation with ethical practices will be crucial to harnessing the benefits of GAI while mitigating its risks and ensuring its positive contribution to society. Responsible Implementations of Generative AI for Multidisciplinary Use highlights both the immense potential of GAI and the ethical challenges it presents. This book demystifies GAI by breaking down complex concepts into accessible language and offering real-world examples that illustrate the implications of its applications. Covering topics such as chatbots, ethical leadership, and the metaverse, this book is an excellent resource for technology professionals and developers, ethicists, policymakers, academicians, researchers, business leaders and executives, legal experts, students, educators, and more.
R 10,636.53
Predictive Analytics and Generative AI for Data-Driven Marketing Strategies
In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies.This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data.• Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making.• Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context.• Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions.• Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.
R 2,674.00
