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Generative Machine Learning Models in Medical Image Computing
Generative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics.Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing.
R 7,407.26
Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
R 3,636.34
Frontiers in Fake Media Generation and Detection
The book presents recent advances in the generation and detection of fake multimedia. It also presents some frontiers in defensive techniques in front of skillfully cloned media. The ultimate purpose of the research direction presented by this book is to build up a trustworthy media network benefited by an iron dome in front of media clones’ attacks. The book focusses on (1) applications of deep generative models in the generation of fake multimedia, and (2) cyber-defensive and detective techniques in front of cyberattacks. The book is composed of three parts: (i) introduction, (ii) fake media generation, and (iii) fake media detection.
R 5,872.94
State-of-the-Art Deep Learning Models in TensorFlow
Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks.The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning.Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office.What You Will LearnTake advantage of the built-in support of the Google Colab ecosystemWork with TensorFlow data setsCreate input pipelines to feed state-of-the-art deep learning modelsCreate pipelined state-of-the-art deep learning models with clean and reliable Python codeLeverage pre-trained deep learning models to solve complex machine learning tasksCreate a simple environment to teach an intelligent agent to make automated decisionsWho This Book Is ForReaders who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab
R 1,824.00
Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
R 11,182.64
Generative Adversarial Networks for Image-to-Image Translation
Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.
R 5,002.00
Unlocking Data with Generative AI and RAG
Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isn’t a distant vision anymore; it’s happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.You’ll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. You’ll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, you’ll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf you’re an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, you’ll be able to make the most of what this book offers.
R 1,628.00
Time Series Forecasting Using Generative AI
"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies."The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.Who this book is for:Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
R 1,832.00
Beginner’s Guide to Generative AI
This book is the essential guide for anyone curious about AI's creative power. In the rapidly evolving landscape of artificial intelligence, generative AI stands out as one of the most transformative technologies of our time. Designed for beginners and requiring no prior knowledge of AI, this book breaks down the fundamentals of generative AI, from text and image generation to the workings of models like ChatGPT and Google Bard. The authors provide step-by-step coverage of the essential concepts and techniques that power generative AI. From the basics of how machines learn to generate text and images, to the intricate workings of models like Transformers, ChatGPT, and Google Bard, readers will gain a solid foundation in AI's most cutting-edge tools. Rather than focusing on a single method, the authors introduce a spectrum of generative modeling techniques, including diffusion models, variational autoencoders, and transformers. This comprehensive exposure ensures readers will be well-prepared to understand and adapt to the rapidly evolving AI landscape. In addition, real-world applications of generative AI across various industries are explored including healthcare innovations, business analytics, and legal technology, and the authors provide practical insights and examples that show how generative AI is revolutionizing these fields.
R 1,649.00
Genai on Google Cloud
In today's AI landscape, success depends not just on using large language models but on moving them into production in a way that's scalable, compliant, and cost-effective. GenAI on Google Cloud: Enterprise Generative AI Systems and Agents is your hands-on guide to bridging that gap. Whether you're an ML engineer tackling your first multi-agent application or a product leader responsible for scaling a GenAI strategy, this book offers a practical game plan for taking your LLM applications from promising prototype to enterprise-grade solution. Written by practitioners with deep experience in MLOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows using Google Cloud's Vertex AI--from training and deployment to monitoring and integration. With concrete examples, field-tested frameworks, and honest insights about what actually works, you'll learn how to build production systems that deliver measurable business value. Bridge the prototype-to-production chasm that stalls 75% of LLM projects using systematic frameworks developed across hundreds of deploymentsNavigate the unique complexities of LLM operations through practical guidance on data orchestration, evaluation frameworks, and responsible AI practicesBuild enterprise-grade multimodal systems that handle text, images, and video with robust agent architectures and tool integrationImplement proven optimization strategies for cost management, performance tuning, and production monitoring at scaleApply battle-tested patterns from practitioners who've guided organizations through both breakthrough successes and costly failures
R 1,824.00
Hands-On Generative AI with Transformers and Diffusion Models
Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains.This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work.Learn the fundamentals of classic and modern generative AI techniquesBuild and customize models that can generate text, images, and soundExplore trade-offs between training from scratch and using large, pretrained modelsCreate models that can modify images by transferring the style of other imagesTweak and bend transformers and diffusion models for creative purposesTrain a model that can write text based on your styleDeploy models as interactive demos or services
R 1,824.00
Building Generative AI Agents
The dawn of AI agents is upon us. Tech visionaries like Bill Gates, Andrew Ng, and Vinod Khosla have highlighted the monumental potential of this powerful technology. This book will provide the knowledge and tools necessary to build generative AI agents using the most popular frameworks, such as AutoGen, LangChain, LangGraph, CrewAI, and Haystack. Recent breakthroughs in large language models have opened up unprecedented possibilities. After years of gradual progress in machine learning and deep learning, we are now witnessing novel approaches capable of understanding, reasoning, and generating content in ways that promise to revolutionize nearly every industry. This platform shift is as significant as the advent of mainframes, PCs, cloud computing, mobile technology, and social media. It's why the world's largest technology companies - like Microsoft, Apple, Google, and Meta - are making enormous investments in this category. While chatbots like ChatGPT, Claude, and Gemini have demonstrated remarkable potential, the years ahead will see the rise of generative AI agents capable of executing complex tasks on behalf of users. These agents already exhibit capabilities such as running test suites, searching the web for documentation, writing software, answering questions based on vast organized information, and performing intricate web-based tasks across multiple domains. They can autonomously investigate cybersecurity incidents and address complex customer support needs. By integrating skills, knowledge bases, planning frameworks, memory, and feedback loops, these systems can handle many tasks and improve over time. Building Generative AI Agents serves as a high-quality guide for developers to understand when and where AI agents can be useful, their advantages and disadvantages, and practical advice on designing, building, deploying, and monitoring them. What You Will Learn The foundational concepts, capabilities, and potential of AI agents.Recent innovations in large language models that have enabled the development of AI agents.How to build AI agents for launching a product, creating a financial plan, handling customer service, and using Retrieval Augmented Generation (RAG).Essential frameworks for building generative AI agents, including AutoGen, LangChain, LangGraph, CrewAI, and Haystack.Step-by-step guidance on designing, building, and deploying AI agents.Insights into the future of AI agents and their potential impact on various industries. Who This Book Is For Experienced software developers
R 1,254.00
Generative Adversarial Networks in Practice
This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.Key Features: Guides you through the complex world of GANs, demystifying their intricacies Accompanies your learning journey with real-world examples and practical applications Navigates the theory behind GANs, presenting it in an accessible and comprehensive way Simplifies the implementation of GANs using popular deep learning platforms Introduces various GAN architectures, giving readers a broad view of their applications Nurture your knowledge of AI with our comprehensive yet accessible content Practice your skills with numerous case studies and coding examples Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject Takes you through GAN applications across different data types, highlighting their versatility Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge Empowers you with the skills and knowledge needed to confidently use GANs in your projectsPrepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
R 2,408.20
Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disordersBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systemsTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoringEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
R 5,373.00
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Essentials of Generative AI
This book provides a concise yet comprehensive introduction to generative artificial intelligence.The first part explains the foundational technologies and architectures that support the realization of generative models. It covers evolved and deepened elements, word embeddings as a representative example of representation learning, and the Transformer as a network foundation, along with its underlying attention mechanism. Reinforcement learning, which became essential for elevating large-scale language models to language generation models, is also discussed in detail, focusing on essential aspects.The second part deals with language generation. It starts by elucidating language modelsand introduces large-scale language models with broad applications as the foundational architecture of language processing, further discussing language generation models as their evolution. Though not common terminology, in this book, models such as ChatGPT and Llama 2, which are large-scale language models fine-tuned using reinforcement learning, are referred to as generative language models.The third part addresses image generation, discussing variational autoencoders and the remarkable diffusion models. Additionally, it explains Generative Adversarial Networks(GAN). Although GAN poses challenges due to unstable learning, their conceptual framework is widely applicable, especially Wasserstein GAN seems suitable for introducing optimal trans- port distance, which is utilized in various scenarios.This book primarily serves as a companion for researchers or graduate students in machine learning, aiming to help them understand the essence of generative AI and lay the groundwork for advancing their own research.
R 2,381.00
Prompt Engineering for Generative AI
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.Learn how to empower AI to work for you. This book explains:The structure of the interaction chain of your program's AI model and the fine-grained steps in betweenHow AI model requests arise from transforming the application problem into a document completion problem in the model training domainThe influence of LLM and diffusion model architecture-and how to best interact with itHow these principles apply in practice in the domains of natural language processing, text and image generation, and code
R 1,824.00
Generative Adversarial Network
This bookcomprehensively and systematically introduces the theory of generative adversarialnetworks and its applications in image and voice processing. This book consistsof 12 chapters, of which the first four chapters present basic knowledge, including the principle of GAN, optimization objectives, training methods andevaluation indicators. The last eight chapters introduce the applications ofGAN in various vertical fields, covering image generation, video generation, image translation, face image editing, image quality improvement, general imageediting, anti-attack, voice signal processing and other fields. Through readingthis book, readers will thoroughly understand the principles of GAN, variousGAN model designs, and learn to apply GAN for most vision and voice tasks.This book is suitable for the juniorresearchers, students or industrial practitioners in related areas.
R 3,114.00
Next Generation Vehicular Networks, Modeling, Algorithm and Applications
This book proposes the novel network envisions and framework design principles, in order to systematically expound the next generation vehicular networks, including the modelling, algorithms and practical applications. It focuses on the key enabling technologies to design the next generation vehicular networks with various vehicular services to realize the safe, convenient and comfortable driving. The next generation vehicular networks has emerged to provide services with a high quality of experience (QoE) to vehicles, where both better network maintainability and sustainability can be obtained than before.The framework design principles and related network architecture are also covered in this book. Then, the series of research topics are discussed including the reputation based content centric delivery, the contract based mobile edge caching, the Stackelberg game model based computation offloading, the auction game based secure computation offloading, the bargain game based security protection and the deep learning based autonomous driving. Finally, the investigation, development and future works are also introduced for designing the next generation vehicular networks.The primary audience for this book are researchers, who work in computer science and electronic engineering. Professionals working in the field of mobile networks and communications, as well as engineers and technical staff who work on the development or the standard of computer networks will also find this book useful as a reference.
R 5,956.81
Next Generation AI Language Models in Research
In this comprehensive and cutting-edge volume, Jeon and Qureshi 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 revolutionise 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 5,652.87
Introduction to Large Language Models
The book offers an introduction to Large Language Models that bridge foundational natural language processing (NLP) concepts with the advanced techniques underlying large language models (LLMs). It offers a structured exploration of NLP evolution, from rule-based approaches to transformer architectures. Covering key principles such as tokenisation, attention mechanisms, and model architectures (BERT, GPT, T5), the book explains pretraining objectives like masked and causal language modeling. It also addresses optimisation techniques such as LoRA, pruning, and quantisation for efficient LLM deployment. Multi-modal models, including GPT-4 and PaLM-E, are explored alongside retrieval-augmented generation and AI-powered agents.Discusses foundational NLP concepts, theoretical depth, advanced techniques, and real-world applications.Covers perplexity, BLEU, ROUGE, and datasets like SuperGLUE and SQuAD for assessing LLM performance, discusses LoRA, pruning, and quantisation to optimise LLM deployment in resource-constrained settings.Explores GPT-4, PaLM-E, and retrieval-augmented generation, expanding beyond traditional NLP models.Provides Python implementations for fine-tuning, classification, summarisation, and conversational AI tasks.Highlights use cases in text generation, code generation, sentiment analysis, and multimodal AI.This book is an invaluable textbook for students, researchers, and industry professionals seeking a deep technical understanding of LLMs and their applications.
R 5,165.00
Generating Creative Images With DALL-E 3
Learn to craft fine art prints, NFTs, and captivating covers for books and magazines with Dall-E 3 and ChatGPTKey FeaturesExplore Dall-E 3's diverse practical applications across art, design, education, and beyondMaster AI-generated art creation through step-by-step tutorials, ranging from basic to advanced projectsEnhance your prompt crafting skills with the exclusive prompt cheat sheetPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnveil the extraordinary capabilities of the groundbreaking AI model, DALL-E 3, as it transforms text prompts into accurate images. This book addresses the challenge of creating meaningful images by writing prompts, guiding you step by step through creating stunning visual art regardless of your skill level.Prepare to delve deep into the inner workings of DALL-E 3's architecture and training process. With clear explanations, practical tutorials, and real-world examples that can be easily applied, you’ll unlock secrets to creating awe-inspiring AI-generated art, from fine art prints to digital designs. This book provides comprehensive insights into various lens options, camera angles, lighting techniques, and art movements, helping you integrate AI capabilities with your artistic skills. You’ll also learn to create NFTs that can be monetized and gain invaluable insights into designing compelling covers, all within the ethical boundaries of AI-generated art. And with the invaluable prompt cheat sheet by your side, you’ll hone your skills in formulating captivating prompts for diverse purposes.By the end of this book, you’ll have learned how to produce generative AI art at a rapid pace and relatively low cost and push the boundaries of imagination with DALL-E 3.What you will learnMaster DALL-E 3's architecture and training methodsCreate fine prints and other AI-generated art with precisionSeamlessly blend AI with traditional artistryAddress ethical dilemmas in AI artExplore the future of digital creativityImplement practical optimization techniques for your artistic endeavorsWho this book is forWhether you’re an artist looking to integrate AI into your work, a designer seeking new creative horizons, a tech enthusiast intrigued by the intersection of art and artificial intelligence, an educator in the fields of art and technology, or a curious individual venturing into AI-generated art, this book is for you. For anyone interested in the innovative fusion of creativity and technology, the DALL-E 3 Guide to AI Artistry offers invaluable insights and practical skills that you can apply right away.
R 1,368.00
Generative AI in Neurodegenerative Disorders
This book delves into the transformative power of AI in the realm of neurodegenerative diseases,covering topics such as ALS, Huntington’s, Parkinson’s, and Alzheimer’s. Generative AIprovides new opportunities for early diagnosis, precise therapy, and individualized rehabilitation,which are crucial as these conditions remain major obstacles for healthcare providersand researchers.Researchers, physicians, AI developers, and healthcare professionals will find this bookan invaluable resource for understanding how AI is influencing the development of treatmentsfor neurodegenerative diseases. It describes important obstacles and future directions whileproviding insights into the newest breakthroughs, thus bridging the gap between technologyand practical clinical applications. Anyone involved in neurodegenerative healthcare, fromscientists conducting AI-driven medical research to physicians seeking to incorporate AI intopatient care or AI professionals investigating new healthcare applications, will find the informationand insights they need in this comprehensive book.Predictive analytics, biomarker identification, and drug discovery are being transformedby AI-driven models, such as deep neural networks, generative adversarial networks (GANs),and variational autoencoders (VAEs). This book offers a comprehensive examination of thesedevelopments. Robots, wearable sensors, and cognitive therapy platforms are some of theAI-enhanced rehabilitation tools covered, as are AI-integrated cutting-edge technologies likefMRI and MRI, gene-editing methods like CRISPR, and more.In addition to discussing recent technical developments, this book takes a close look atthe data privacy, ethics, and regulatory issues that arise when using AI to study neurodegenerativedisorders. Issues like algorithmic bias, model explainability, and fair AI-driven healthcareare thoroughly investigated in light of the growing usage of AI models in clinical decision-making,mental health applications, and cognitive rehabilitation.
R 5,495.00
Generative Adversarial Networks in Practice
Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key features: Guides you through the complex world of GANs, demystifying their intricacies. Accompanies your learning journey with real-world examples and practical applications. Navigates the theory behind GANs, presenting it in an accessible and comprehensive way. Simplifies the implementation of GANs using popular deep learning platforms. Introduces various GAN architectures, giving readers a broad view of their applications. Nurture your knowledge of AI with our comprehensive yet accessible content. Practice your skills with numerous case studies and coding examples. Reviews advanced GANs such as DCGAN, CGAN, CycleGAN, and more, with clear explanations and practical examples. Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs. Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject. Takes you through GAN applications across different data types, highlighting their versatility. Inspires the reader to explore beyond the book, fostering an environment conducive to independent learning and research. Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge. Empowers you with the skills and knowledge needed to confidently use GANs in your projects.Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
R 4,313.79
Revolutionizing Healthcare 5.0: The Power of Generative AI
This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.
R 7,244.58
Geochemical Mechanics and Deep Neural Network Modeling
The recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders.
R 6,228.00
R 1,824.00
R 367.00
Modern Generative AI with ChatGPT and OpenAI Models
Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAIPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore the theory behind generative AI models and the road to GPT3 and GPT4Become familiar with ChatGPT's applications to boost everyday productivityLearn to embed OpenAI models into applications using lightweight frameworks like LangChainBook DescriptionGenerative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects.What you will learnUnderstand generative AI concepts from basic to intermediate levelFocus on the GPT architecture for generative AI modelsMaximize ChatGPT's value with an effective prompt designExplore applications and use cases of ChatGPTUse OpenAI models and features via API callsBuild and deploy generative AI systems with PythonLeverage Azure infrastructure for enterprise-level use casesEnsure responsible AI and ethics in generative AI systemsWho this book is forThis book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
R 1,693.00
Deep Cognitive Networks
Although deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways.To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing.This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions.
R 2,377.95
R 1,008.00
Generative AI and LLMs
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
R 5,955.00
Landscape of Next Generation Sequencing Using Pattern Recognition
This book focuses on an eminent technology called next generation sequencing (NGS) which has entirely changed the procedure of examining organisms and will have a great impact on biomedical research and disease diagnosis. Numerous computational challenges have been brought on by the rapid advancement of large-scale next-generation sequencing (NGS) technologies and their application. The term ""biomedical imaging"" refers to the use of a variety of imaging techniques (such as X-rays, CT scans, MRIs, ultrasounds, etc.) to get images of the interior organs of a human being for potential diagnostic, treatment planning, follow-up, and surgical purposes. In these circumstances, deep learning, a new learning method that uses multi-layered artificial neural networks (ANNs) for unsupervised, supervised, and semi-supervised learning, has attracted a lot of interest for applications to NGS and imaging, even when both of these data are used for the same group of patients.The three main research phenomena in biomedical research are disease classification, feature dimension reduction, and heterogeneity. AI approaches are used by clinical researchers to efficiently analyse extremely complicated biomedical datasets (e.g., multi-omic datasets. With the use of NGS data and biomedical imaging of various human organs, researchers may predict diseases using a variety of deep learning models. Unparalleled prospects to improve the work of radiologists, clinicians, and biomedical researchers, speed up disease detection and diagnosis, reduce treatment costs, and improve public health are presented by using deep learning models in disease prediction using NGS and biomedical imaging. This book influences a variety of critical disease data and medical images.
R 5,480.01
Deep Learning and Linguistic Representation
The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear.Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge.Key Features:combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics.is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas.provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.
R 9,296.89
DeepSeek in Action
From fundamental concepts to advanced implementations, this book thoroughly explores the DeepSeek-V3 model, focusing on its Transformer-based architecture, technological innovations, and applications.The book begins with a thorough examination of theoretical foundations, including self-attention, positional encoding, the Mixture of Experts mechanism, and distributed training strategies. It then explores DeepSeek-V3’s technical advancements, including sparse attention mechanisms, FP8 mixed-precision training, and hierarchical load balancing, which optimize memory and energy efficiency. Through case studies and API integration techniques, the model's high-performance capabilities in text generation, mathematical reasoning, and code completion are examined. The book highlights DeepSeek’s open platform and covers secure API authentication, concurrency strategies, and real-time data processing for scalable AI applications. Additionally, the book addresses industry applications, such as chat client development, utilizing DeepSeek’s context caching and callback functions for automation and predictive maintenance.This book is aimed primarily at AI researchers and developers working on large-scale AI models. It is an invaluable resource for professionals seeking to understand the theoretical underpinnings and practical implementation of advanced AI systems, particularly those interested in efficient, scalable applications.
R 9,379.78
R 1,527.00
R 2,469.00
Generative AI in Higher Education
Chan and Colloton’s book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.Since November 2022, every conversation in higher education has involved ChatGPT and its impact on all aspects of teaching and learning. The book explores the necessity of AI literacy tailored to professional contexts, assess the strengths and weaknesses of incorporating ChatGPT in curriculum design, and delve into the transformation of assessment methods in the GenAI era. The authors introduce the Six Assessment Redesign Pivotal Strategies (SARPS) and an AI Assessment Integration Framework, encouraging a learner-centric assessment model. The necessity for well-crafted AI educational policies is explored, as well as a blueprint for policy formulation in academic institutions. Technical enthusiasts are catered to with a deep dive into the mechanics behind GenAI, from the history of neural networks to the latest advances and applications of GenAI technologies.With an eye on the future of AI in education, this book will appeal to educators, students and scholars interested in the wider societal implications and the transformative role of GenAI in pedagogy and research.
R 1,942.00
