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R 458.00
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
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
Large Language Models
An in-depth history of Large Language Models and what their ubiquity, disruption, and creativity mean from a wider sociopolitical perspective. In November 2022, ChatGPT swept the globe with a mixed frenzy of excitement and anxiety. Was this a step closer to reaching singularity or just another marvel in machine learning? Author Stephan Raaijmakers provides a comprehensive introduction to Large Language Models (LLMs), describing what exactly they are capable of from a technical and creative standpoint. This concise volume covers everything from the architecture of LLM neural networks to the limitations of LLMs to how our governments can regulate this technology. In explaining how exactly LLMs learn from data sets, Raaijmakers defangs the more sensational arguments we may be familiar with. Instead, he offers a more grounded approach to how this groundbreaking and increasingly ubiquitous form of artificial intelligence will shape our society for years to come.
R 517.00
R 611.00
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
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
R 474.00
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
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
Understanding Large Language Models
This book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them.The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse.After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects. What You Will LearnGrasp the underlying concepts of LLMsGain insight into how the concepts and approaches of NLP have evolved over the yearsUnderstand transformer models and attention mechanismsExplore different types of LLMs and their applicationsUnderstand the architectures of popular LLMsDelve into misconceptions and concerns about LLMs, as well as how to best utilize themWho This Book Is ForAnyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learning
R 1,026.00
Hands-On Large Language Models
AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning
R 1,978.00
R 764.00
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 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
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
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
Quick Start Guide to Large Language Models
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application."--Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,628.00
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
Large Language Models - The Hard Part
Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Partsoffers a clear, practical examination of the limitations developers and ML engineers face when building LLM-powered applications. With a focus on implementation pitfalls (not just capabilities) this book provides actionable strategies supported by reproducible Python code and open source tools.Readers will learn how to navigate key obstacles in system integration, input management, testing, safety, and cost control. Designed for engineers and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs.Design testing strategies for nondeterministic systemsManage input formatting and long-context retrievalAddress output inconsistency and structural unreliabilityImplement safety and content moderation frameworksExplore alignment challenges and mitigation techniquesLeverage open source models and optimize costs
R 1,978.00
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
Artificial Intelligence and Large Language Models
Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time.Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitionersIllustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers.
R 3,115.47
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
Transformers and Large Language Models
This book is a hands-on guide to understanding the foundations, architectures, and real-world applications of transformers and large language models in modern AI. The book begins by laying the foundations of generative AI architectures, tokenization, encoding, and classical modeling techniques. Initial chapters address the evolution from feed-forward networks and recurrent neural networks to long short-term memory (LSTM), setting the stage for the revolutionary transformer architecture. The core of the book focuses on transformers, introducing the encoder-decoder framework, attention mechanisms, positional encodings, and the internal workings of multi-head attention, normalization, and multi-layer perceptrons. Readers gain insight into advanced techniques such as rotary positional embeddings (RoPE), mixture of experts (MoE), and knowledge distillation, alongside practical training strategies like self-supervised learning, fine-tuning, and reinforcement learning with human feedback. Popular models from OpenAI, DeepSeek, and other vendors are examined to highlight the evolution of the LLM landscape. Building on these foundations, the text explores methods for model customization, including parameter-efficient fine-tuning (LoRA, adapters), text generation strategies, prompt engineering, and quantization. Retrieval-Augmented Generation (RAG) is introduced as a critical innovation for grounding LLMs in external knowledge, with detailed evaluation techniques for retrieval and generation. Finally, the book ventures into Agentic AI, demonstrating protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) interactions with practical coding examples. In conclusion, this book serves as both a practical guide, equipping readers with the technical depth and applied strategies needed to design, fine-tune, and deploy cutting-edge transformers and large language models for real-world applications. What we will learn: Ø Understand the foundations of AI, ML pipelines, tokenization, encoding, and early neural architectures. Ø Explore transformers in depth--encoder-decoder design, attention mechanisms, and advanced embedding methods. Ø Learn modern LLM advancements like RoPE, MoE, SLMs, fine-tuning strategies, and evaluation techniques. Ø Master practical customization through prompt engineering, PEFT methods, quantization, and text generation. nWho this book is for: Data scientists, ML engineers, AI researchers, and developers exploring Transformers and large language models.
R 1,368.00
Large Language Models and Prompt Engineering
This book provides a structured and accessible introduction to the theory, design, and real-world use of large language models (LLMs). As generative AI systems become embedded across industries, this book offers readers a balanced perspective that combines technical foundations with practical guidance and critical reflection.Beginning with the fundamentals of LLMs and prompt engineering, this book progressively explores advanced prompting techniques, application and interaction frameworks, and the underlying architectures that power modern language models. Dedicated chapters examine how LLMs are being applied in high-impact domains such as healthcare, customer support, and education, highlighting design principles, implementation considerations, and illustrative case studies. The book also addresses model fine-tuning, evaluation methods, and the ethical, social, and regulatory challenges associated with deploying LLMs at scale.This book supports readers in:· Understanding prompt engineering strategies and application frameworks· Gaining insight into contemporary LLM architecture and capabilities· Designing and evaluating LLM-based systems across real-world domains· Considering ethics, safety, and human-centered aspects of LLM deploymentThis book is a practical, accessible resource for those seeking to work thoughtfully and responsibly with LLMs in both academic and applied settings.
R 5,165.00
Mastering Large Language Models
This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.Starting with the fundamentals--LLM architecture, tokenization, APIs, and fine-tuning--the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance--helping readers move confidently from basic understanding to complex applications.Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability--and innovation into impact.What you will learn: Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.Design AI agents capable of planning and executing complex workflows for automation and decision-making.Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio. Who this book is for: Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.
R 1,368.00
Artificial Intelligence and Large Language Models
Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time.Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitionersIllustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers.
R 7,579.38
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
Intermediate Python and Large Language Models
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you'll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You'll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. By the end of the book, you'll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction. What You Will Learn Understand the fundamentals of LangChain and Python for LLM development Know advanced AI workflows, including fine-tuning and memory management Build AI-powered applications such as chatbots, retrieval systems, and automation tools Know deployment strategies and performance optimization for real-world use Use best practices for scalability, security, and responsible AI implementation Unlock the full potential of LLMs and take your AI development skills to the next level Who This Book Is For Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks
R 1,254.00
Designing Large Language Model Applications
Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.You'll learn:Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful productsHow to develop an intuition about the Transformer architecture and the impact of each architectural decisionWays to adapt pretrained language models to your own domain and use casesHow to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrumEffective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniquesHow to interface language models with external tools and integrate them into an existing software ecosystem
R 1,824.00
Large Language Models for Developers
This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture’s attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES• Covers the full lifecycle of working with LLMs, from model selection to deployment• Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization• Teaches readers to enhance model efficiency with advanced optimization techniques• Includes companion files with code and images -- available from the publisher
R 1,482.00
Large Language Models and Prompt Engineering
This book provides a structured and accessible introduction to the theory, design, and real-world use of large language models (LLMs). As generative AI systems become embedded across industries, this book offers readers a balanced perspective that combines technical foundations with practical guidance and critical reflection.Beginning with the fundamentals of LLMs and prompt engineering, this book progressively explores advanced prompting techniques, application and interaction frameworks, and the underlying architectures that power modern language models. Dedicated chapters examine how LLMs are being applied in high-impact domains such as healthcare, customer support, and education, highlighting design principles, implementation considerations, and illustrative case studies. The book also addresses model fine-tuning, evaluation methods, and the ethical, social, and regulatory challenges associated with deploying LLMs at scale.This book supports readers in:· Understanding prompt engineering strategies and application frameworks· Gaining insight into contemporary LLM architecture and capabilities· Designing and evaluating LLM-based systems across real-world domains· Considering ethics, safety, and human-centered aspects of LLM deploymentThis book is a practical, accessible resource for those seeking to work thoughtfully and responsibly with LLMs in both academic and applied settings.
R 2,674.00
R 336.00
R 794.00
Large Language Models for Chemists
In recent years, LLMs (such as Claude, DeepSeek, Llama and other transformer-based models) have emerged as powerful tools in chemistry, enabling new approaches to scientific discovery. While many chemists, from undergraduate students to researchers find these AI models interesting, they may lack certain knowledge base to better integrate these tools into their daily research.Large Language Models for Chemists breaks down that barrier by demystifying how LLMs work in an accessible way and showing, step-by-step, how they can be applied to solve real chemistry problems. Written in a friendly, tutorial style, the book assumes only a basic background in chemistry and minimal programming experience. It begins by gently introducing artificial intelligence and machine learning concepts in lay terms, building up to the inner workings of LLMs without heavy math. Readers will learn how these models “think” and generate text, gaining an intuitive understanding of concepts like neural networks, transformers, and training data using analogies and simple diagrams. Crucially, each concept is reinforced with chemistry-focused examples – from understanding chemical nomenclature and reactions as a “language,” to exploring how an LLM can suggest synthetic routes or explain spectral data.Beyond theory, this book emphasizes practical application. Each chapter includes hands-on tutorials and case studies that invite readers to experiment with real tools. Using open-source libraries (such as RDKit for cheminformatics and standard Python machine learning frameworks), readers will walk through projects like predicting molecular properties with the aid of an LLM, generating novel compound ideas, analyzing research papers, and even using an LLM as a conversational chemistry assistant. For example, one case study guides the reader in using an LLM to mine a chemistry literature database and then write Python code to analyze reaction trends, mirroring cutting-edge research where LLMs assist in code generation and data mining for chemical discovery.
R 11,112.25
R 916.00
Quick Start Guide to Large Language Models
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI AgentsMaster advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAIConstruct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasetsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mindDiagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,199.00
