Search results for "Deep Generative Modeling"
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Generative Hate
Venturing under the hood and across platform ecosystems, this book demonstrates how text-to-image models offer a new playground for racism, misogyny, and xenophobia. From photorealistic bigotry to algorithmic caricatures, cutting-edge AI tools are not just reproducing hate but remaking it in novel ways. Drawing together a technical understanding of AI systems and a deep familiarity with both hate and meme cultures, this book offers specific examples, lays out the key characteristics of generative hate, and suggests how content moderation might become more AI-attuned. Generative Hate is a call to action for scholars, technologists, and citizens to understand and confront this emerging phenomenon.
R 1,766.07
Generative Artificial Intelligence and Explainable Artificial Intelligence for Real-world Applications
This book integrates the concepts of explainable artificial intelligence, and generative explainable artificial intelligence, showcasing how explainability can be incorporated into generative models. It highlights the importance of making generative models transparent, offering strategies to achieve this through interpretability techniques.Focuses on the principles and methodologies of explainable artificial intelligence, providing readers with a thorough understanding of how artificial intelligence models can be made transparent and interpretable.Explores explainable artificial intelligence techniques, including feature importance, SHAP values, LIME, and counterfactual explanations.Offers an in-depth examination of generative artificial intelligence, covering cutting-edge advancements in generative models like GANs, VAEs, and transformer-based architectures.Bridges the gap between theoretical concepts and their practical applications, making advanced artificial intelligence technologies accessible to the readers.Includes numerous case studies and real-world examples that demonstrate the successful application of generative artificial intelligence, and explainable artificial intelligence.The text is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, biomedical engineering, and information technology.
R 10,257.00
Robotics and Generative AI in Healthcare
The book reviews the transformative impact of robotics and generative AI technologies on healthcare. It covers the engineering principles and key technologies that support modern healthcare robotics. By explaining the core components and their functions, the book provides readers with a deep understanding of how these technologies contribute to medical innovations. It also explores the basics of generative AI, focusing on its underlying models and practical applications, including its role in transforming diagnostics. The book explains the applications of generative AI in diagnostic precision, and in improving patient care. Furthermore, the practical aspects of integrating these technologies into healthcare settings are addressed, including the challenges and opportunities involved. It discusses the ethical considerations surrounding the use of robotics and AI, such as data privacy, bias, and regulatory compliance. The final chapters deal with future developments, emerging trends, and potential innovations in the field, offering a forward-looking perspective on how robotics and AI will continue to shape the future of healthcare.
R 8,093.47
Domain-Specific Modeling
"[The authors] are pioneers. . . . Few in our industry have their breadth of knowledge and experience." —From the Foreword by Dave Thomas, Bedarra Labs Domain-Specific Modeling (DSM) is the latest approach to software development, promising to greatly increase the speed and ease of software creation. Early adopters of DSM have been enjoying productivity increases of 500–1000% in production for over a decade. This book introduces DSM and offers examples from various fields to illustrate to experienced developers how DSM can improve software development in their teams. Two authorities in the field explain what DSM is, why it works, and how to successfully create and use a DSM solution to improve productivity and quality. Divided into four parts, the book covers: background and motivation; fundamentals; in-depth examples; and creating DSM solutions. There is an emphasis throughout the book on practical guidelines for implementing DSM, including how to identify the necessary language constructs, how to generate full code from models, and how to provide tool support for a new DSM language. The example cases described in the book are available the book's Website, www.dsmbook.com, along with, an evaluation copy of the MetaEdit+ tool (for Windows, Mac OS X, and Linux), which allows readers to examine and try out the modeling languages and code generators. Domain-Specific Modeling is an essential reference for lead developers, software engineers, architects, methodologists, and technical managers who want to learn how to create a DSM solution and successfully put it into practice.
R 4,460.00
Load Modelling and Generation in IP-based Networks
Andrey Kolesnikov proposes an interesting unified approach and corresponding tools for modelling and effective generation of realistic workloads and traffic in networks. As a result of the general applicability in IP-based networks, the outcome of his research can be used for different service interfaces in combination with various workload models and modelling techniques. His work is both broad and deep in focus in order to demonstrate the application of the proposed approach in different realistic scenarios.
R 2,015.00
Multi-Modal Human Modeling, Analysis and Synthesis
In today’s world, where intelligent technologies are deeply transforming human-computer interaction and virtual reality, multi-modal human modeling, analysis and synthesis have become central topics in computer vision. As application scenarios grow increasingly complex, new technologies continue to emerge to address these challenges. These techniques demand systematic summarization and practical guidance.To meet this need, Multi-Modal Human Modeling, Analysis and Synthesis aims to adopt a structured perspective, building a comprehensive technical framework for multi-modal human modeling, analysis and synthesis—progressing from local details to holistic perspectives, and from face features to body dynamics.This book begins by examining the anatomy structures and characteristics of human faces and bodies, then analyzes how traditional methods and deep learning approaches provide robust optimization solutions for modeling. For example, it explores how to address challenges in face recognition caused by lighting changes, occlusions, face expressions and aging, as well as methods for body localization, reconstruction, recognition and anomaly detection in multi-modal scenarios. It also explains how multi-modal data can drive realistic face and body synthesis. A standout feature is its focus on Huawei’s MindSpore framework, bridging the gap between algorithms and engineering through practical case studies. From building face detection and recognition pipelines with the MindSpore toolkit to accelerating model training via automatic parallel computing, and solving large language model (LLM) training challenges, each step is supported by reproducible code and design logic.Designed for researchers and engineers in computer vision and AI, this book balances theoretical foundations with industry-ready technical details. Whether you aim to enhance the reliability of biometric recognition, explore creative possibilities in virtual-real interactions or optimize the deployment of deep learning frameworks, this guide serves as an essential link between academic advancements and real-world applications.
R 6,677.09
Generative AI: Techniques, Models and Applications
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from:Clear explanations of advanced concepts.Practical implementation insights.Current industry applications.Ethical framework discussions.Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
R 5,910.02
Rewired
The world’s most influential management consulting firm, McKinsey & Company, updates its essential guide to AI transformation In the newly revised second edition of Rewired: The McKinsey Playbook on How Leading Companies Win with Technology and AI, five experienced McKinsey leaders deliver an essential and incisive update to their widely read guide to planning and implementing successful AI transformations. The book walks you through the six capabilities every company must develop to succeed with AI: transformation roadmapping that targets real value, a talent bench of highly skilled experts, an operating model that allows you to move at pace, a distributed technology environment that is flexible, data embedded throughout your organization, , and adoption and scaling that enable solutions to realize meaningful gains. This new edition offers brand new insights into cutting-edge AI solutions―and what it takes to implement them―as well as the new economics of AI transformations. You’ll also discover how to build up the “tech muscles” of your AI transformation leaders, how to develop effective human-AI collaboration models, how to revolutionize software develop with AI agents, and how to industrialize AI so it can safely and effectively scale for the age of AI. Inside the book: More than 30 real-world case studies and examples of large, established companies that have successfully navigated these sorts of transformations, with deep dives into Toyota, DBS Bank, Freeport-McMoRan, and LATAM AirlinesComprehensive “how-to” transformation guides and frameworks you can put into practice immediately, including 100 detailed exhibitsDeep dives into the role of leadership at the C-suite and domain levels, as well as practical tips for navigating and avoiding the pitfalls that slow down most transformations Perfect for the C-suite, managers, executives, board members, and other business leaders, Rewired is a must-read playbook for professionals ready to roll up their sleeves and do the hard work necessary to reimagine their company and generate transformational value.
R 1,051.00
Generative Adversarial Learning: Architectures and Applications
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
R 6,261.60
Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications
This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:• an exclusive section on essential preprocessing approaches for the data-driven model,• a detailed overview of data-driven model applications to power system planning and operational activities,• specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and• showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks.This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.
R 4,452.07
Next Generation AI Language Models in Research
In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised.The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation.This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.
R 2,711.00
Generative AI
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI’s ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
R 5,890.24
Generative Artificial Intelligence
This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models.The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation.By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will LearnWhat is Generative Artificial Intelligence?What are text-to-image synthesis techniques and conditional image generation?What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques?What are text-to-video models and how do you tune them?What are large language models, and how do you tune them? Who This Book Is ForThose with intermediate to advanced technical knowledge in artificial intelligence and machine learning
R 1,140.00
Building Business-Ready Generative AI Systems
Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models. Get with your book: PDF copy, AI Assistant, and Next-Gen Reader free.Key FeaturesBuild an adaptive, context-aware AI controller with advanced memory strategiesEnhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT)Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fitBook DescriptionStandalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you’ll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system’s intelligence with multimodal capabilities—image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.What you will learnImplement an AI controller with a conversation AI agent and orchestrator at its coreBuild contextual awareness with short-term, long-term, and cross-session memoryDesign cross-domain automation with multimodal reasoning, image generation, and voice featuresExpand a CoT agent by integrating consumer-memory understandingIntegrate cutting-edge models of your choice without disrupting your existing GenAISysConnect to real-time external data while blocking security breachesWho this book is forThis book is for AI and Machine Learning Engineers seeking to enhance their understanding of Generative AI and its enterprise applications. It will particularly benefit those interested in building AI agents, creating advanced orchestration systems, and leveraging AI for automation in marketing, production, and logistics. Software architects and enterprise developers looking to build scalable AI-driven systems will also find immense value in this guide. No prior superintelligence experience is necessary, but familiarity with AI concepts is recommended.
R 1,628.00
Databricks Certified Generative AI Engineer Associate Study Guide
As generative AI becomes central to modern data and machine learning workflows, earning the Databricks Certified Generative AI Engineer Associate certification signals both credibility and technical fluency. This study guide prepares you for this sought-after certification with comprehensive coverage of core GenAI and LLM concepts, practical data management using Delta Lake, and retrieval-augmented generation (RAG) application workflows on the Databricks platform. Written by AI expert Rajaniesh Kaushikk, this book combines hands-on labs, real-world examples, and exam-aligned content to help you build and deploy effective GenAI applications. From prompt engineering and RAG-based solutions to model governance with Unity Catalog, MLflow tracking, and leveraging Hugging Face models in GenAI workflows, this guide supports both your certification journey and real-world AI development. Understand the fundamentals of generative AI, LLMs, and vector databasesPrepare and manage large-scale data using Delta Lake and the data lakehouseBuild and deploy generative AI models with Databricks and MLflowExplore RAG for enhanced model performanceImplement responsible AI practices and model governance with Unity Catalog
R 1,824.00
R 763.00
Large Language Models: A Deep Dive
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs—their intricate architecture, underlying algorithms, and ethical considerations—require thorough exploration, creating a need for a comprehensive book on this subject.This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios.Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models.This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.Key Features:over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learningover 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applicationsOver 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deploymentOver 200 benchmarks covering LLM performance across various tasks, ethical considerations, mult
R 2,381.00
Generative AI in Software Development
Generative AI in Software Development: Practical Ways AI is Building the Future of Code explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn’t a theoretical or academic deep dive it’s a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer. What sets this book apart is its balance: it's not just for hardcore engineers, nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful, but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's becoming essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.
R 8,572.00
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
R 2,705.66
Generative AI: Innovations and Applications Across Industries
This book explores Generative AI, covering its transformative applications and innovative technologies. Each chapter meticulously examines Generative AI's impact across diverse domains, including creative fields, healthcare, education, entertainment, finance, and more.The book begins by establishing the foundation for comprehending Generative AI, presenting fundamental concepts, tracing its historical progression, and analyzing key technologies like deep neural networks (DNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) in Chapter 1. In Chapter 2 we explore Generative AI's role in revolutionizing creative domains like writing, visual arts, and music composition. Chapter 3 delves into its impact on business and marketing, reshaping strategies and operations through AI-generated content and personalized experiences. In Chapter 4, we investigate Generative AI's influence on healthcare, accelerating drug discovery, personalized medicine, and medical imaging. Education is spotlighted in Chapter 5, showcasing Generative AI's contributions to personalized learning and interactive educational materials. Chapter 6 examines its impact on entertainment and media, while Chapters 7 through 11 delve into legal, financial, industrial, and environmental applications, addressing ethical considerations and future trends. Finally, Chapter 12 concludes with case studies and expert insights, offering readers a comprehensive understanding of Generative AI's potential and societal implications. Join us on this enlightening journey through the diverse landscapes of Generative AI, where innovation knows no bounds and the future is limited only by our imagination.In the end, readers will gain a comprehensive understanding of the transformative potential and societal implications of Generative AI.What you will learn:What are the foundational concepts and technological advancements behind Generative AI.What are large language modelsUnderstand Generative AI's role in reshaping business landscapes.What is the future of Generative AI and its potential impact. Who this book is for:AI Professionals, machine learning engineers and software professionals seeking a comprehensive understanding of Generative AI across various business domains.
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Deep Learning through Sparse and Low-Rank Modeling
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
R 2,941.00
R 305.00
Practical MLflow for Generative AI on Databricks
Bridge the gap between cutting-edge research and the practical operationalization of generative AI. This handy guide demystifies the complexities of managing GenAI applications using leading open source platform MLflow on Databricks.What sets this book apart is its focus on the requirements of managing and deploying GenAI applications. Authors Nuwan Ganganath, Julie Nguyen, and Chang Shi Lim—seasoned ML and AI specialists from Databricks—take you through each stage of the development of an agentic chatbot, sharing best practices, innovative techniques, and common challenges along the way. By combining deep technical insights with interactive exercises and actionable real-world code examples, they ensure that you're equipped to implement robust GenAI applications in modern enterprise environments.Understand MLflow fundamentals, including core components and the tool's application to GenAI on the Databricks platformBuild, track, and manage GenAI applications using MLflow, from prompts to the overall agentic chainOptimize and evaluate GenAI applications through a combination of automated LLM-assisted judges and human-in-the-loop reviewDeploy production-ready GenAI applications, leveraging Databricks' scalable infrastructure and monitoring capability
R 1,978.00
Artificial Intelligence in Action
This comprehensive book dives deep into the current landscape of AI, exploring its fundamental principles, development challenges, potential risks, and the cutting-edge breakthroughs that are propelling it forward. Artificial intelligence (AI) is rapidly transforming industries and societies worldwide through groundbreaking innovations and real-world applications. Starting with the core concepts, the book examines the various types of AI systems, generative AI models, and the complexities of machine learning. It delves into the programming languages driving AI development, data pipelines, model creation and deployment processes, while shedding light on issues like AI hallucinations and the intricate path of machine unlearning.The book then showcases the remarkable real-world applications of AI across diverse domains. From preventing job displacement and promoting environmental sustainability, to enhancing disaster response, drone technology, and even nuclear energy innovation, it highlights how AI is tackling complex challenges and driving positive change.The book also explores the double-edged nature of AI, recognizing its tremendous potential while cautioning about the risks of misuse, unintended consequences, and the urgent need for responsible development practices. It examines the intersection of AI and fields like operating system design, warfare, and semiconductor technology, underscoring the wide-ranging implications of this transformative force.As the quest for artificial general intelligence (AGI) and superintelligent AI systems intensifies, the book delves into cutting-edge research, emerging trends, and the pursuit of multimodal, explainable, and causally aware AI systems. It explores the symbiotic relationship between AI and human creativity, the rise of user-friendly "casual AI," and the potential of AI to tackle open-ended tasks.This is an essential guide for understanding the profound impact of AI on our world today and its potential to shape our future. From the frontiers of innovation to the challenges of responsible development, this book offers a comprehensive and insightful exploration of the remarkable real-world applications and innovations driving the AI revolution.
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R 1,367.00
Generative AI in Software Development
Generative AI in Software Development: Practical Ways AI is Building the Future of Code explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn’t a theoretical or academic deep dive it’s a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer. What sets this book apart is its balance: it's not just for hardcore engineers, nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful, but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's becoming essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.
R 2,308.00
AI-Based Forecasting of Solar Photovoltaics Power Generation
The widespread deployment of photovoltaics (PV) systems has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy systems. Driven by a combination of supportive regulatory frameworks, government incentive programs, technical developments, and increasing environmental awareness, the adoption of PV technologies has witnessed remarkable growth in recent years. However, the rapid integration of distributed PV systems into existing electricity grid infrastructure introduces new challenges, particularly concerning voltage regulation, reverse power flow, and congestion within the electricity grid. These issues are intensified when PV systems are integrated without proper strategy. In this context, solar PV power forecasting has become an essential tool for ensuring the reliable and efficient integration of solar PV systems into power systems. Artificial intelligence (AI) and machine learning (ML) offer means to forecast PV power and energy generation based on historical data of PV generation, meteorological data, and/or weather forecasts. AI-Based Forecasting of Solar Photovoltaics Power Generation blends theoretical knowledge with practical case studies, which serves as a comprehensive and timely contribution to the rapidly evolving field of solar PV forecasting. It covers topics such as data collection and processing, solar forecasting based on statistical time-series, machine and deep learning, hybrid and probabilistic approaches, model optimization, hyperparameter tuning, and solar PV forecasting for energy system integration and control. As solar PV systems become increasingly integrated into energy systems, a dedicated book on PV generation forecasting is incredibly useful, making this book an important resource for energy system operators, policymakers, researchers, and students seeking to improve the reliability, resiliency, and efficiency of solar PV systems and the broader systems into which they are integrated.
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Artificial Intelligence and Large Language Models
With the latest developments in generative artificial intelligence (AI) and large language model (LLM) applications, like ChatGPT and Gemini, millions of people have started using them in their personal and professional lives. Interest in them continues to grow and they are becoming the go-to applications for students, academics, engineers, lawyers, designers, writers, media experts, programmers, politicians, artists, and citizens across the globe. However, understanding these applications can be challenging with little advice out there. This book cuts through the jargon to explain the ‘what’, ‘why’ and ‘how’ of generative AI and LLM applications.This book summarizes experiences from a variety of applications, relevant to our daily lives. It brings together scientific-based experience reports from different perspectives into a one-stop title. With an international outlook and featuring global case studies, it offers insights into the beneficial use of LLMs and generative AI including future trends for how the journey of using such amazing tools will continue in the future. This book details the history of AI, reviews LLM literature and covers neural computing and the technical foundation of LLMs. Furthermore, the use and control of LLMs in education is covered. Through reading this book, the reader will develop a full understanding of AI and LLM applications, including knowledge of the key issues of trust, legality, security and technical foundations.Artificial Intelligence and Large Language Models: A Scientific Perspective will appeal to students and academics, as well as people interested in the use of LLMs and generative AI for professional or everyday tasks, including those in Human Factors, Human-Computer Interaction, Computer Science, Software Engineering and Industrial Engineering.
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Generative AI on Aws
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. In this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, machine learning practitioners, business analysts, data engineers, and data scientists find a practical way to use this exciting new technology. You'll learn the generative AI project lifecycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), model quantization, optimization, and deployment. You'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and video. You'll also be able to make better-informed decisions for your company regarding generative AI and learn how to build working prototypes quickly. While the focus is on AWS, this book is a great resource for learning generative AI fundamentals and applying these models to real-world applications. Apply generative AI to your business use cases Determine which generative AI models to use based on the task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets Align generative AI models to human values with reinforcement learning from human feedback Use techniques like retrieval-augmented generation to augment your model Explore libraries such as LangChain and React to develop agents and actions Learn about multimodal models such as Stable Diffusion for image and video generation Get hands-on with Amazon Bedrock, the AWS generative AI managed service
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R 519.00
Practical Guide to Generative AI Using Amazon Bedrock
This comprehensive guide gives you the knowledge and skills you need to excel in Generative AI. From understanding the fundamentals to mastering techniques, this book offers a step-by-step approach to leverage Amazon Bedrock to build, deploy, and secure Generative AI applications. The book presents structured chapters and practical examples to delve into key concepts such as prompt engineering, retrieval-augmented generation, and model evaluation. You will gain profound insights into the Amazon Bedrock platform. The book covers setup, life cycle management, and integration with Amazon SageMaker. The book emphasizes real-world applications, and provides use cases and best practices across industries on topics such as text summarization, image generation, and conversational AI bots. The book tackles vital topics including data privacy, security, responsible AI practices, and guidance on navigating governance and monitoring challenges while ensuring adherence to ethical standards and regulations. The book provides the tools and knowledge needed to excel in the rapidly evolving field of Generative AI. Whether you're a data scientist, AI engineer, or business professional, this book will empower you to harness the full potential of Generative AI and drive innovation in your organization. What You Will Learn Understand the fundamentals of Generative AI and Amazon BedrockBuild Responsible Generative AI applications leveraging Amazon BedrockKnow techniques and best practicesSee real-world applicationsIntegrate and manage platformsHandle securty and governance issuesEvaluate and optimze modelsGain future-ready insightsUnderstand the project life cycle when building Generative AI Applications Who This Book Is For Data scientistys, AI/ML engineers and architects, software developers plus AI enthusiasts and studenta and educators, and leaders who want to evangelize within organizatios
R 1,254.00
R 878.00
R 428.00
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.
R 9,316.15
Statistical Modeling in Biomedical Research
This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in:Next generation sequence data analysisDeep learning, precision medicine, and their applicationsLarge scale data analysis and its applicationsBiomedical research and modelingSurvival analysis with complex data structure and its applications.
R 3,627.00
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
R 1,978.00
Generative AI Design Patterns
Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs.This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs.Design around the limitations of LLMsEnsure that generated content follows a specific style, tone, or formatMaximize creativity while balancing different types of riskBuild agents that plan, self-correct, take action, and collaborate with other agentsCompose patterns into agentic applications for a variety of use cases
R 1,824.00
Multimodal Generative AI
This book stands at the forefront of AI research, offering a comprehensive examination of multimodal generative technologies. Readers are taken on a journey through the evolution of generative models, from early neural networks to contemporary marvels like GANs and VAEs, and their transformative application in synthesizing realistic images and videos. In parallel, the text delves into the intricacies of language models, with a particular on revolutionary transformer-based designs. A core highlight of this work is its detailed discourse on integrating visual and textual models, laying out state-of-the-art techniques for creating cohesive, multimodal AI systems. “Multimodal Generative AI” is more than a mere academic text; it’s a visionary piece that speculates on the future of AI, weaving through case studies in autonomous systems, content creation, and human-computer interaction. The book also fosters a dialogue on responsible innovation in this dynamic field. Tailored for postgraduates, researchers, and professionals, this book is a must-read for anyone vested in the future of AI. It empowers its readers with the knowledge to harness the potential of multimodal systems in solving complex problems, merging visual understanding with linguistic prowess. This book can be used as a reference for postgraduates and researchers in related areas.
R 9,347.44
