Decoding Large Language Models
DOWNLOAD
Download Decoding Large Language Models PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Decoding Large Language Models book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Decoding Large Language Models
DOWNLOAD
Author : Irena Cronin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-31
Decoding Large Language Models written by Irena Cronin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-31 with Computers categories.
Explore the architecture, development, and deployment strategies of large language models to unlock their full potential Key Features Gain in-depth insight into LLMs, from architecture through to deployment Learn through practical insights into real-world case studies and optimization techniques Get a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challenges Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Explore the architecture and components of contemporary LLMs Examine how LLMs reach decisions and navigate their decision-making process Implement and oversee LLMs effectively within your organization Master dataset preparation and the training process for LLMs Hone your skills in fine-tuning LLMs for targeted NLP tasks Formulate strategies for the thorough testing and evaluation of LLMs Discover the challenges associated with deploying LLMs in production environments Develop effective strategies for integrating LLMs into existing systems Who this book is for If 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.
A Beginner S Guide To Large Language Models
DOWNLOAD
Author : Enamul Haque
language : en
Publisher: Enamul Haque
Release Date : 2024-07-25
A Beginner S Guide To Large Language Models written by Enamul Haque and has been published by Enamul Haque this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts Step into the revolutionary world of artificial intelligence with "A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts." Whether you're a curious individual or a professional seeking to leverage AI in your field, this book demystifies the complexities of large language models (LLMs) with engaging, easy-to-understand explanations and practical insights. Explore the fascinating journey of AI from its early roots to the cutting-edge advancements that power today's conversational AI systems. Discover how LLMs, like ChatGPT and Google's Gemini, are transforming industries, enhancing productivity, and sparking creativity across the globe. With the guidance of this comprehensive and accessible guide, you'll gain a solid understanding of how LLMs work, their real-world applications, and the ethical considerations they entail. Packed with vivid examples, hands-on exercises, and real-life scenarios, this book will empower you to harness the full potential of LLMs. Learn to generate creative content, translate languages in real-time, summarise complex information, and even develop AI-powered applications—all without needing a technical background. You'll also find valuable insights into the evolving job landscape, equipping you with the knowledge to pursue a successful career in this dynamic field. This guide ensures that AI is not just an abstract concept but a tangible tool you can use to transform your everyday life and work. Dive into the future with confidence and curiosity, and discover the incredible possibilities that large language models offer. Join the AI revolution and unlock the secrets of the technology that's reshaping our world. "A Beginner's Guide to Large Language Models" is your key to understanding and mastering the power of conversational AI. Introduction This introduction sets the stage for understanding the evolution of artificial intelligence (AI) and large language models (LLMs). It highlights the promise of making complex AI concepts accessible to non-technical readers and outlines the unique approach of this book. Chapter 1: Demystifying AI and LLMs: A Journey Through Time This chapter introduces the basics of AI, using simple analogies and real-world examples. It traces the evolution of AI, from rule-based systems to machine learning and deep learning, leading to the emergence of LLMs. Key concepts such as tokens, vocabulary, and embeddings are explained to build a solid foundation for understanding how LLMs process and generate language. Chapter 2: Mastering Large Language Models Delving deeper into the mechanics of LLMs, this chapter covers the transformer architecture, attention mechanisms, and the processes involved in training and fine-tuning LLMs. It includes hands-on exercises with prompts and discusses advanced techniques like chain-of-thought prompting and prompt chaining to optimise LLM performance. Chapter 3: The LLM Toolbox: Unleashing the Power of Language AI This chapter explores the diverse applications of LLMs in text generation, language translation, summarisation, question answering, and code generation. It also introduces multimodal LLMs that handle both text and images, showcasing their impact on various creative and professional fields. Practical examples and real-life scenarios illustrate how these tools can enhance productivity and creativity. Chapter 4: LLMs in the Real World: Transforming Industries Highlighting the transformative impact of LLMs across different industries, this chapter covers their role in healthcare, finance, education, creative industries, and business. It discusses how LLMs are revolutionising tasks such as medical diagnosis, fraud detection, personalised tutoring, and content creation, and explores the future of work in an AI-powered world. Chapter 5: The Dark Side of LLMs: Ethical Concerns and Challenges Addressing the ethical challenges of LLMs, this chapter covers bias and fairness, privacy concerns, misuse of LLMs, security threats, and the transparency of AI decision-making. It also discusses ethical frameworks for responsible AI development and presents diverse perspectives on the risks and benefits of LLMs. Chapter 6: Mastering LLMs: Advanced Techniques and Strategies This chapter focuses on advanced techniques for leveraging LLMs, such as combining transformers with other AI models, fine-tuning open-source LLMs for specific tasks, and building LLM-powered applications. It provides detailed guidance on prompt engineering for various applications and includes a step-by-step guide to creating an AI-powered chatbot. Chapter 7: LLMs and the Future: A Glimpse into Tomorrow Looking ahead, this chapter explores emerging trends and potential breakthroughs in AI and LLM research. It discusses ethical AI development, insights from leading AI experts, and visions of a future where LLMs are integrated into everyday life. The chapter highlights the importance of building responsible AI systems that address societal concerns. Chapter 8: Your LLM Career Roadmap: Navigating the AI Job Landscape Focusing on the growing demand for LLM expertise, this chapter outlines various career paths in the AI field, such as LLM scientists, engineers, and prompt engineers. It provides resources for building the necessary skillsets and discusses the evolving job market, emphasising the importance of continuous learning and adaptability in a rapidly changing industry. Thought-Provoking Questions, Simple Exercises, and Real-Life Scenarios The book concludes with practical exercises and real-life scenarios to help readers apply their knowledge of LLMs. It includes thought-provoking questions to deepen understanding and provides resources and tools for further exploration of LLM applications. Tools to Help with Your Exercises This section lists tools and platforms for engaging with LLM exercises, such as OpenAI's Playground, Google Translate, and various IDEs for coding. Links to these tools are provided to facilitate hands-on learning and experimentation.
Large Language Models A Deep Dive
DOWNLOAD
Author : Uday Kamath
language : en
Publisher: Springer Nature
Release Date : 2024-08-20
Large Language Models A Deep Dive written by Uday Kamath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-20 with Computers categories.
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 learning Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications Over 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 deployment Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently
Large Language Models For Developers
DOWNLOAD
Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-26
Large Language Models For Developers written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-26 with Computers categories.
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
Large Language Models
DOWNLOAD
Author : Jagdish Krishanlal Arora
language : en
Publisher: Jagdish Krishanlal Arora
Release Date : 2024-03-28
Large Language Models written by Jagdish Krishanlal Arora and has been published by Jagdish Krishanlal Arora this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-28 with Computers categories.
Journey into the World of Advanced AI: From Concept to Reality Step into a realm where artificial intelligence isn't just a concept but a transformative force reshaping our world. Whether you're a tech enthusiast, a researcher, or an AI newcomer, this captivating exploration will draw you into the revolutionary domain of Large Language Models (LLMs). Imagine a future where machines understand and generate human-like text, answering questions, creating content, and assisting in ways once dreamt of only in science fiction. This isn't the future; it's now. The evolution of LLMs from early language models to sophisticated transformers like the GPT series by OpenAI is a story of relentless innovation and boundless potential. With insightful chapters that dissect the trajectory of LLMs, you'll uncover the intricate journey starting from early algorithms to the groundbreaking GPT series. Discover the multifaceted applications of LLMs across various industries, their remarkable benefits, and the challenges that researchers and developers face in quest of creating even more advanced systems. Dive into the specifics of language model evolution, from Word2Vec to the marvels of modern-day GPT. Learn how LLMs are revolutionizing fields such as customer service, content creation, and even complex problem-solving. Their ability to process and generate human-like language opens doors to innovations beyond our wildest dreams. This book isn't just a technical manual; it's a glimpse into the dynamic world of AI, offering a balanced view of the excitement and challenges that accompany such groundbreaking technology. Ready to be part of the journey that transforms how we interact with technology? This book will ignite your curiosity and broaden your understanding of the powerful engines driving the AI revolution.
Designing Large Language Model Applications
DOWNLOAD
Author : Suhas Pai
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-03-06
Designing Large Language Model Applications written by Suhas Pai and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-06 with Computers categories.
Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more. Understand how to prepare datasets for training and fine-tuning Develop an intuition about the Transformer architecture and its variants Adapt pretrained language models to your own domain and use cases Learn effective techniques for fine-tuning, domain adaptation, and inference optimization Interface language models with external tools and data and integrate them into an existing software ecosystem
Large Language Models
DOWNLOAD
Author : John Atkinson-Abutridy
language : en
Publisher: CRC Press
Release Date : 2024-10-17
Large Language Models written by John Atkinson-Abutridy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-17 with Computers categories.
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 six 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 NLP. 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.
Large Language Models Llms In Protein Bioinformatics
DOWNLOAD
Author : Dukka B. KC
language : en
Publisher: Springer Nature
Release Date : 2025-07-02
Large Language Models Llms In Protein Bioinformatics written by Dukka B. KC and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-02 with Computers categories.
This book presents a comprehensive collection of methods, resources, and studies that use large language models (LLMs) in the field of protein bioinformatics. Reflecting the swift pace of LLM development today, the volume delves into numerous LLM-based tools to investigate proteins science, from protein language models to the prediction of protein-ligand binding sites. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure success in future research. Authoritative and practical, Large Language Models (LLMs) in Protein Bioinformatics serves as an ideal guide for scientists seeking to tap into the potential of artificial intelligence in this vital area of biological study.
Generative Ai Techniques Models And Applications
DOWNLOAD
Author : Rajan Gupta
language : en
Publisher: Springer Nature
Release Date : 2025-03-26
Generative Ai Techniques Models And Applications written by Rajan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Computers categories.
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from: Clear explanations of advanced concepts. Practical implementation insights. Current industry applications. Ethical framework discussions. Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
Hands On Large Language Models
DOWNLOAD
Author : Jay Alammar
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-09-11
Hands On Large Language Models written by Jay Alammar and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-11 with Computers categories.
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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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 to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)