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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
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
Advanced Applications of Generative AI and Natural Language Processing Models
The rapid advancement of technology in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and generative AI, presents a challenge for academic scholars to remain informed with the most current information and with the latest techniques and applications. The lack of comprehensive resources also hinders researchers' ability to gain a deep understanding of these subjects and effectively implement these technologies. Advanced Applications of Generative AI and Natural Language Processing Models is a comprehensive resource which offers an effective solution by investigating cutting-edge developments in NLP and generative AI. It provides insights into how these technologies function, their benefits, and the associated challenges.This book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI for students, researchers, and professionals in AI, NLP, and computer science. It uses real-world examples and practical applications to empower scholars to apply their knowledge and solve complex problems.Advanced Applications of Generative AI and Natural Language Processing Models is a complete guide that equips researchers with the knowledge needed to utilize NLP and generative AI, including deep learning for NLP, generative models for text generation, language modelling and sentiment analysis, machine translation and multilingual models, ethical considerations in NLP, conversational AI applications, and case studies and real-world applications.
R 10,468.56
Challenges and Applications of Generative Large Language Models
Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.
R 5,297.00
Large Language Models for Linguists
This book bridges linguistics and technology, offering an in-depth exploration of Large Language Models (LLMs) tailored for linguists. It examines how Artificial Intelligence (AI) is trained using language as data, often overlooking linguistic nuance, and empowers linguists to shape AI development to preserve diverse linguistic and cultural heritages.Key Features: Accessible to All: No prior knowledge of technology or coding required, making it ideal for anyone transitioning into AI for languages. Demystifies LLMs: Covers essential concepts like Natural Language Processing (NLP), named entity recognition (NER), hallucinations, fine-tuning, and custom LLMs for languages. Global Scope: Includes research, datasets, and LLM models for languages such as Portuguese, Yoruba, Tamil, Hindi, Swahili, Spanish, Korean, Arabic, Indigenous languages, Sign Languages, and more. Cultural Context: Explores how historical trauma, colonialism, migration, and resilience shape language, emphasizing the need for AI to respect this complexity. Practical Playbook: Guides linguists and cultural stewards in creating datasets and benchmarks that honor their communities, showcasing how vocabulary reflects cultural identity. Online Resources: Offers tools, datasets, and community support for building AI tailored to specific languages at LLMLinguist.org.Ideal for humanities scholars, computational linguists, and technologists seeking to develop language models that preserve the cultural richness of the world's voices.
R 6,960.00
Power of Large Language Models and AI in the Digital Age
Large language models (LLMs) represent a profound breakthrough in artificial intelligence. More than just statistical tools, these vast neural networks undergo an intensive training process that unlocks unexpected, emergent abilities. Models like ChatGPT are now demonstrating a surprising grasp of reasoning, semantics, and real-world concepts, leading many researchers to ask: are we witnessing the first sparks of Artificial General Intelligence (AGI)? These models are doing more than just revolutionizing natural language processing; they are forcing us to reconsider the boundaries of machine intelligence. While their applications in chatbots, complex problem-solving, and content creation are already reshaping our digital world, their deeper implications point toward an entirely new era of AI. To understand these emergent capabilities and confront the AGI question, a deep dive into the core technology is essential. This co-authored book provides that crucial foundation. The authors explore the significance and capabilities of LLMs, alongside the immense ethical, social, and security challenges that arise as these models grow more powerful. For anyone seeking a comprehensive guide to navigate this new territory, The Power of Large Language Models and AI in the Digital Age: Technologies, applications, security and ethics is a valuable resource. It is essential reading for data scientists, researchers from academia and industry, lecturers, and advanced students in AI, computer science, and data science who will shape the future of intelligent systems.
R 4,559.00
Foundation Models for Natural Language Processing
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
R 2,198.00
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
Large Language Models
This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction:•You will explore the fascinating world of LLMs, from its foundations to its most powerful applications.•You will learn how to build your own simple applications with some of the LLMs.Designed to guide you step by step, with 6 chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in natural language processing. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
R 7,587.51
Foundation Models for Natural Language Processing
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
R 1,832.00
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
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
Large Language Models for Linguists
This book bridges linguistics and technology, offering an in-depth exploration of Large Language Models (LLMs) tailored for linguists. It examines how Artificial Intelligence (AI) is trained using language as data, often overlooking linguistic nuance, and empowers linguists to shape AI development to preserve diverse linguistic and cultural heritages.Key Features: Accessible to All: No prior knowledge of technology or coding required, making it ideal for anyone transitioning into AI for languages. Demystifies LLMs: Covers essential concepts like Natural Language Processing (NLP), named entity recognition (NER), hallucinations, fine-tuning, and custom LLMs for languages. Global Scope: Includes research, datasets, and LLM models for languages such as Portuguese, Yoruba, Tamil, Hindi, Swahili, Spanish, Korean, Arabic, Indigenous languages, Sign Languages, and more. Cultural Context: Explores how historical trauma, colonialism, migration, and resilience shape language, emphasizing the need for AI to respect this complexity. Practical Playbook: Guides linguists and cultural stewards in creating datasets and benchmarks that honor their communities, showcasing how vocabulary reflects cultural identity. Online Resources: Offers tools, datasets, and community support for building AI tailored to specific languages at LLMLinguist.org.Ideal for humanities scholars, computational linguists, and technologists seeking to develop language models that preserve the cultural richness of the world's voices.
R 2,674.00
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 2,674.00
Large Language Models
This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher.
R 1,254.00
Large Language Models
This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction:•You will explore the fascinating world of LLMs, from its foundations to its most powerful applications.•You will learn how to build your own simple applications with some of the LLMs.Designed to guide you step by step, with 6 chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in natural language processing. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
R 2,670.29
Generative AI and Large Language Models: Opportunities, Challenges, and Applications
This book provides a comprehensive exploration of the transformative impact of AI technologies across diverse fields. From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs). With a focus on both opportunities and challenges, the book examines the architectural challenges of transformer-based models, the ethical implications of AI, and the importance of language-specific adaptations, particularly for low-resource languages like Arabic. It also highlights the role of AI in code development, multimodal applications, and its integration with intellectual property frameworks. This book is an essential resource for researchers, practitioners, and policymakers seeking to understand and harness the potential of AI to drive innovation and global progress.
R 8,387.99
R 842.00
Advancements in Machine Learning and Natural Language Processing: Innovations and Applications
This book discovers groundbreaking advancements in artificial intelligence with innovative solutions for real-world challenges. This book showcases state-of-the-art methodologies like deep learning and transfer learning to tackle sentiment analysis, fake news detection, and multi-dialectal named entity recognition, with a special focus on Arabic language technologies. Bridging the gap between research and practice, it highlights topics such as knowledge extraction, AI ethics, and the societal impacts of big data. Targeted at researchers, educators, and professionals, it serves as a vital guide for beginners and a comprehensive reference for experts seeking to stay ahead in the rapidly evolving field of AI.
R 9,495.18
Concept Drift in Large Language Models
This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence.Examines concept drift in AI, particularly its impact on large language modelsAnalyses how concept drift affects large language models and its theoretical and practical consequencesCovers detection methods and practical implementation challenges in language modelsShowcases examples of concept drift in GPT models and lessons learnt from their performanceIdentifies future research avenues and recommendations for practitioners tackling concept drift in large language models
R 5,613.19
Decoding Large Language Models
Explore the architecture, development, and deployment strategies of large language models to unlock their full potentialKey FeaturesGain in-depth insight into LLMs, from architecture through to deploymentLearn through practical insights into real-world case studies and optimization techniquesGet a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications.You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP.By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.What you will learnExplore the architecture and components of contemporary LLMsExamine how LLMs reach decisions and navigate their decision-making processImplement and oversee LLMs effectively within your organizationMaster dataset preparation and the training process for LLMsHone your skills in fine-tuning LLMs for targeted NLP tasksFormulate strategies for the thorough testing and evaluation of LLMsDiscover the challenges associated with deploying LLMs in production environmentsDevelop effective strategies for integrating LLMs into existing systemsWho this book is forIf you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.
R 1,693.00
Introduction to Python and Large Language Models
Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming.The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components.You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots.In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs.What You’ll LearnUnderstand the basics of Python and the features of Python 3.11Explore the essentials of NLP and how do they lay the foundations for LLMs.Review LLM components.Develop basic apps using LLMs and Python.Who This Book Is ForData analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.
R 2,412.29
Concept Drift in Large Language Models
This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence.Examines concept drift in AI, particularly its impact on large language modelsAnalyses how concept drift affects large language models and its theoretical and practical consequencesCovers detection methods and practical implementation challenges in language modelsShowcases examples of concept drift in GPT models and lessons learnt from their performanceIdentifies future research avenues and recommendations for practitioners tackling concept drift in large language models
R 2,674.00
Challenges in Large Language Model Development and AI Ethics
The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.
R 12,942.10
Future of Learning with Large Language Models
The book covers theoretical foundations on how LLMs can enhance learning, cognitive reinforcement, improving learning efficiency, and personalization in learning, applications across the curriculum, teacher training and support for LLM integration, using in assessment and evaluation, and measuring the impact and affordances of LLMs. It acknowledges the challenges that come with integrating LLMs into education and will address the responsible development and deployment strategies to ensure that the models become powerful tools for good in the hands of educators. It explores potential research directions, such as the development of domain-specific models, and the creation of ethical frameworks for LLM use in education. As education enters an era of AI-enablement, this visionary book equips teachers, administrators, technologists, and policymakers with an authoritative guide to harnessing the power of large language models. Readers will discover how these advanced systems can expand access to quality education, tailor learning experiences, and nurture the innovators and critical thinkers of tomorrow, and glimpse into the future of learning and education with LLMs.
R 7,234.88
Advances using AI - The Next Wave
Advances using AI – The Next Wave offers a timely and comprehensive exploration of the latest breakthroughs and future directions in Artificial Intelligence, presenting a forward-looking perspective on how AI is reshaping industries and daily life. This essential volume brings together expert insights on AI's practical applications, ethical considerations, and its profound impact on society, making complex concepts accessible and relevant.Delve into critical discussions on AI's role in transforming rural education, decoding human emotions, navigating uncertainty with Probabilistic AI, and optimizing global travel and logistics. Discover how AI is driving sustainable 6G networks, revolutionizing financial auditing, and quietly powering an invisible revolution that demands our attention. Each chapter provides in-depth analysis and real-world examples, empowering readers to understand, implement, and critically evaluate AI's rapidly evolving landscape.This book is indispensable for AI professionals, researchers, academics, policymakers, students, and anyone eager to grasp the current state and future potential of Artificial Intelligence across diverse sectors.
R 2,958.01
Generative AI Technologies, Multiliteracies, and Language Education
Generative AI Technologies, Multiliteracies, and Language Education is a comprehensive edited volume that examines the integration of Generative AI (GenAI) technologies within the framework of multiliteracies pedagogies to enhance language teaching and learning.This collection of chapters offers an in-depth understanding of how GenAI can transform language education through theoretical insights and empirical research. Featuring contributions from leading scholars in the field, this innovative volume provides both foundational concepts and innovative practices alongside evidence-based methodologies and practical strategies for educators, enhancing both teaching effectiveness and student engagement in multiliteracies environments. The book investigates the role that GenAI grounded in multiliteracies can play in language education, providing readers with comprehensive theoretical and pedagogical bases for the use of GenAI technologies in language teaching and learning, empirical evidence from research work, and solid guidelines and recommendations for practice and implementation in the language classroom.Generative AI Technologies, Multiliteracies, and Language Education will be of interest to those involved in teaching, researching, or developing curriculum that integrates technology and multiliteracies with language learning.
R 9,296.60
Integrating Artificial Intelligence Technology into Language Teacher Education
This volume provides five exploratory studies on the constraints and affordances of artificial intelligence (AI), specifically examining ChatGPT’s applications among preservice teachers preparing to teach multilingual learners. Through detailed analysis of classroom implementation, student perceptions, and pedagogical outcomes, this collection offers groundbreaking insights into how large language models can transform teacher preparation programs. Three studies present firsthand accounts of preservice teachers’ experiences with ChatGPT, revealing a pragmatic orientation where adoption hinges on student motivation, accelerated English language development, and ease of use. The remaining two studies investigate ChatGPT-produced text as an instructional tool, highlighting its potential for fostering critical analysis while emphasizing the necessity of thoughtful integration through careful teacher planning.This volume speaks directly to graduate students, teacher educators, educational technology specialists, TESOL professionals, and researchers in applied linguistics and AI ethics. It bridges multiple disciplines including educational technology, second language acquisition, teacher preparation, multilingual education, and artificial intelligence in education. The research presented in this volume contributes to ongoing conversations about technological innovation in classrooms, ethical considerations in AI implementation, and evolving best practices for preparing teachers to work effectively with multilingual populations in technology-enhanced learning environments.The chapters in this book were originally published as a special issue of Computers in the Schools.
R 9,312.90
Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
R 1,955.66
Building Personality-Driven Language Models
This book provides an innovative exploration into the realm of artificial intelligence (AI) by developing personalities for large language models (LLMs) using psychological principles. Aimed at making AI interactions feel more human-like, the book guides you through the process of applying psychological assessments to AIs, enabling them to exhibit traits such as extraversion, openness, and emotional stability. Perfect for developers, researchers, and entrepreneurs, this work merges psychology, philosophy, business, and cutting-edge computing to enhance how AIs understand and engage with humans across various industries like gaming and healthcare. The book not only unpacks the theoretical aspects of these advancements but also equips you with practical coding exercises and Python code examples, helping you create AI systems that are both innovative and relatable. Whether you’re looking to deepen your understanding of AI personalities or integrate them into commercial applications, this book offers the tools and insights needed to pioneer this exciting frontier.
R 1,832.00
R 1,008.00
Generative AI Technologies, Multiliteracies, and Language Education
Generative AI Technologies, Multiliteracies, and Language Education is a comprehensive edited volume that examines the integration of Generative AI (GenAI) technologies within the framework of multiliteracies pedagogies to enhance language teaching and learning.This collection of chapters offers an in-depth understanding of how GenAI can transform language education through theoretical insights and empirical research. Featuring contributions from leading scholars in the field, this innovative volume provides both foundational concepts and innovative practices alongside evidence-based methodologies and practical strategies for educators, enhancing both teaching effectiveness and student engagement in multiliteracies environments. The book investigates the role that GenAI grounded in multiliteracies can play in language education, providing readers with comprehensive theoretical and pedagogical bases for the use of GenAI technologies in language teaching and learning, empirical evidence from research work, and solid guidelines and recommendations for practice and implementation in the language classroom.Generative AI Technologies, Multiliteracies, and Language Education will be of interest to those involved in teaching, researching, or developing curriculum that integrates technology and multiliteracies with language learning.
R 2,224.90
R 458.00
Rethinking Language Education in the Age of Generative AI
Rethinking Language Education in the Age of Generative AI bridges the gap between theory, research, and practice in AI and language education. Through conceptual pieces, empirical studies, and practical applications, this book provides critical insights and implications for reimagining language education in the age of generative AI.The contributors explore a wide range of issues, reflections, and innovations in AI and language education across diverse contexts, including English as a Second Language (ESL), English as a Foreign Language (EFL), foreign language learning, postsecondary pathways programs for international students, and language teacher education programs. Topics examined include critical AI literacy, GenAI-informed second language teaching and assessment, teacher and student perceptions, tool development for language learning, as well as ethical considerations, policies, and guidelines. The book incorporates interdisciplinary perspectives, such as L2/foreign language studies, education, and applied linguistics, as well as global insights from countries like the United States, Canada, South Korea, Thailand, Indonesia, and the Philippines.This book is essential for students and researchers seeking to leverage AI to enhance language teaching and learning in innovative, critical, ethical, and responsible ways.
R 9,341.06
Research Trends in Discourse Analysis and Language Acquisition in the Artificial Intelligence Era
This volume reflects on this topic and adds information about the different approaches within discourse analysis and language acquisition that consider artificial intelligence (AI). This book challenges the traditional approach to discourse and language acquisition, incorporating technology and AI's role in language studies. It is distinctive in the sense that it offers several chapters that focus on linguistic analysis considering AI. The particular benefits offered by its content, scope, organization and/or educational features are that it provides two aspects that are crucial in Linguistics: on the one hand, it shows the use of AI in discourse analysis and, on the other, it focuses on the advances in language acquisition with the incorporation of AI. The needs it aims to satisfy are to show examples of the use of AI and the advances in language that include AI, demonstrating that linguistic studies can be complemented with AI.
R 2,235.00
Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
R 7,565.42
Deep Learning Research Applications for Natural Language Processing
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
R 11,359.26
Generative AI for Sign Language Recognition and Translation
Sign languages differ fundamentally from spoken and written languages, with their own grammar, syntax, and three-dimensional expression involving hand gestures, facial expressions, body movements, and spatial relationships. These non-manual elements are crucial in conveying grammatical structures, nuances, and emotional tones, making sign languages uniquely complex communication systems. This book provides a comprehensive foundation for understanding the linguistic structures of sign languages and explores the application of artificial intelligence (AI) techniques - ranging from classical machine learning to deep learning and generative AI - for developing effective sign language translation systems. It offers an end-to-end overview, covering linguistic fundamentals, available datasets, text-to-sign and speech-to-sign translation, vision-based sign recognition, pose estimation, and video-based sign language generation. Dedicated chapters focus on model architectures, dataset curation strategies, evaluation metrics, benchmarking tools, and human-centered design approaches for accessible communication systems. Ethical considerations and responsible AI practices are also discussed to promote the development of inclusive and equitable sign language technologies. Complemented by Python code examples, downloadable resources, and implementation insights, this book serves as a practical guide for researchers, engineers, students, and technology professionals aiming to develop AI-powered sign language systems. The multidisciplinary content also supports linguists, accessibility advocates, and application developers working on inclusive language technologies. With its broad coverage and practical orientation, this book is suited to academic and industry professionals in artificial intelligence, computer vision, natural language processing, human-computer interaction, speech technology, and accessibility research, as well as students and early-career researchers seeking a well-rounded introduction to AI-driven sign language translation. By bridging AI methodologies with real-world sign language applications, this book promotes the development of inclusive AI systems supporting communication accessibility for diverse populations.
R 4,132.00
Deep Learning Research Applications for Natural Language Processing
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
R 8,775.56
Large Language Models
Are you eager to explore the latest breakthrough in artificial intelligence, particularly the domain of large language models (LLMs)? This book is your go-to guide for understanding the core foundations and advanced techniques of LLMs. This comprehensive resource offers a complete understanding of LLM developments, from pre-training to fine-tuning. It elaborates on the classic Transformer architecture, its adaptations for LLMs, and the full training process, including data collection, cleaning, and preparation. From the book, readers can also learn how to fine-tune LLMs to follow human instructions and align with human values and intentions, ensuring safer and more ethical AI behavior. Furthermore, it helps readers discover effective prompting strategies, such as in-context learning and chain-of-thought, to enhance LLM capabilities and solve complex tasks. Suitable for both beginners and experienced professionals, this book is an invaluable resource for navigating the dynamic field of LLMs, offering a concise yet comprehensive exploration of the subject. The translation was originally done using artificial intelligence. Subsequently, a comprehensive human revision was done to ensure content accuracy and coherence throughout the book.
R 8,059.00
Rethinking Language Education in the Age of Generative AI
Rethinking Language Education in the Age of Generative AI bridges the gap between theory, research, and practice in AI and language education. Through conceptual pieces, empirical studies, and practical applications, this book provides critical insights and implications for reimagining language education in the age of generative AI.The contributors explore a wide range of issues, reflections, and innovations in AI and language education across diverse contexts, including English as a Second Language (ESL), English as a Foreign Language (EFL), foreign language learning, postsecondary pathways programs for international students, and language teacher education programs. Topics examined include critical AI literacy, GenAI-informed second language teaching and assessment, teacher and student perceptions, tool development for language learning, as well as ethical considerations, policies, and guidelines. The book incorporates interdisciplinary perspectives, such as L2/foreign language studies, education, and applied linguistics, as well as global insights from countries like the United States, Canada, South Korea, Thailand, Indonesia, and the Philippines.This book is essential for students and researchers seeking to leverage AI to enhance language teaching and learning in innovative, critical, ethical, and responsible ways.
R 2,646.87
Exploration of the Intersection of Corpus Linguistics and Language Science
Corpus linguistics plays a crucial role in advancing our understanding of language by providing empirical data-driven insights across various linguistic disciplines. By analyzing large collections of real-world texts, corpus methodologies enhance language teaching, improve comprehension of language processing in the brain, and offer new perspectives on literary analysis. In natural language processing (NLP), corpora serve as the foundation for training AI models, enabling more accurate machine translation, speech recognition, and text analysis. These applications not only deepen our theoretical understanding of language but also drive innovations in education, technology, and communication. As linguistic research continues to evolve, corpus linguistics remains a powerful tool for bridging the gap between theory and real-world language use. Exploration of the Intersection of Corpus Linguistics and Language Science explores the intersection of corpus linguistics with key areas of language sciences, including language teaching, psycholinguistics, literary analysis, and natural language processing. Through innovative research and empirical studies, it highlights how corpus methodologies enhance linguistic theory, inform practical applications, and drive advancements in language-related fields. Covering topics such as phonemic awareness, cognitive patterns, and writing performance, this book is an excellent resource for linguists, psycholinguists, language scientists, educators, literacy professionals, researchers, professionals, scholars, academicians, and more.
R 7,001.91
Modern Computational Models of Semantic Discovery in Natural Language
Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities?Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery.This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.
R 8,329.35
