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Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don't understand anything I can't build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you'll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you'll really understand it because you built it yourself! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA

In the rapidly evolving AI landscape, customer service and support professionals find themselves in a prime position to take advantage of this innovative technology to drive customer success. The AI Revolution in Customer Service and Support is a practical guide for professionals who want to harness the power of generative AI within their organizations to create more powerful customer and employee experiences. This book is designed to equip you with the knowledge and confidence to embrace the AI revolution and integrate the technology, such as large language models (LLMs), machine learning, predictive analytics, and gamified learning, into the customer experience. Start your journey toward leveraging this technology effectively to optimize organizational productivity. A portion of the book's proceeds will be donated to the nonprofit Future World Alliance, dedicated to K-12 AI ethics education. IN THIS BOOK YOU'LL LEARN About AI, machine learning, and data science How to develop an AI vision for your organization How and where to incorporate AI technology in your customer experience fl ow About new roles and responsibilities for your organization How to improve customer experience while optimizing productivity How to implement responsible AI practices How to strengthen your culture across all generations in the workplace How to address concerns and build strategies for reskilling and upskilling your people How to incorporate games, play, and other techniques to engage your agents with AI Explore thought experiments for the future of support in your organization “Insightful & comprehensive—if you run a service & support operation, put this book on your essential reading list right now!” —PHIL WOLFENDEN, Cisco, VP, Customer Experience “This book is both timely and relevant as we enter an unprecedented period in our industry and the broader world driven by Generative AI. The magnitude and speed of change we're experiencing is astounding and this book does an outstanding job balancing technical knowledge with the people and ethical considerations we must also keep front of mind.” —BRYAN BELMONT, Microsoft, Corporate VP, Customer Service & Support “The authors of this book are undoubtedly on the front lines of operationalizing Gen AI implementations in customer support environments… and they know undoubtedly that at its core, support is about people and genuine human connections. This book walks you through their journey to keep people at the center of this technical tsunami.” —PHAEDRA BOINODIRIS, Author, AI for the Rest of Us

This book provides a theoretical framework to allow educators, researchers, and policymakers to better understand computer‑generated writing and the policy and pedagogical implications of generative AI.Generative AI, such as ChatGPT and Gemini, has substantially disrupted educational spaces, forcing educators, policymakers, and other stakeholders to reconsider writing and how it should be used in education. Responding to this disruption, this book provides technically sound guidance on how various stakeholders should engage with generative AI. After providing a foundational and technical discussion of the technology, this book directly addresses the educational context. Informed by theories of learning and knowledge transfer and utilizing rhetorical theories of writing, this book assesses the impact of AI on student learning, student performance, and academic honesty and integrity. In doing so, the book outlines how generative AI can be both a help and a hindrance for students, enabling readers to craft informed and meaningful policies and successfully integrate AI in the composition classroom.This book will be of interest to scholars in the fields of Rhetoric and Composition, Technical Writing, Communication Studies, Linguistics, and TESOL, as well as to Education and Machine Learning policymakers, program directors, and researchers.

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.

About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. “Understanding natural language understanding”, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision. This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more.

This book presents a novel methodological framework for analysing governmental discourse. It involves combining pragmatist perspectives on language with computational sociolinguistics and large language models (LLMs).The first half discusses traditional critical approaches to investigating discursive practices, principally those employing Critical Discourse Analysis (CDA) and those based on methods developed by Michel Foucault. These are critiqued in terms of pragmatist views on meaning, which are rarely taken up in this area. It is argued that to understand the grounding of social structures and power relations in discourse, we must begin with a systematic account of how meaning is contextually fixed. It is proposed that a pragmatist reading of Foucault's arguments about governmentality offers a productive framework for discourse analysis. To illustrate the advantages of this framework, the book presents a case study of the British government's adoption of resilience, sustainability, and wellbeing discourses in the period 2000-2020. A dataset of 179 million tokens sampled from approximately 170,000 government documents is used to illustrate how this framework can be combined with natural language processing (NLP) to make robust inferences.This study will be of interest to both sociologists interested in language and in the methodological potential of recent developments in NLP. Importantly, the book demonstrates how LLMs can be harnessed to bring new perspectives to long-standing sociological questions.

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.

This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will Learn Be able to answer the question: What are Large Language Models? Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases Know the best practices for effective implementation Know the metrics and frameworks essential for evaluating the performance of Large Language Models Who This Book Is For An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs

The Holy Grail of AI is artificial generative intelligence, a computer that can think human-like. However, human thinking is qualitatively more complex than computer calculations. So, the ultimate goal of AI cannot be achieved. Not quite. This book shows that a model of human-like, meaningful processing can be introduced based on a theory of cognition (how human processing can be abstracted in a series of events), semiotics (what signs are and what kind of distinctions can be communicated by signs), and computer science (how all this can be realized as a procedure). The emerging model offers a solution to the problem of artificial intelligence, not by itself, but in collaboration with the human agent by augmenting its intelligence. But there is more to it than that. Because of the fundamental nature of signs, the semiotic concept of meaning can be transformative for AI research. The book comprehensively covers several applications, including language processing, analyzing integrative negation processes, and solving mathematical problems. It delves into the intricate characteristics of the meaningful processing problem and the fascinating journey that led to its solution. The book provides insight into the historical background of the problem and the solution, enriching the reader's understanding and engagement. The text is self-contained. All necessary technical terms are explained.

This two-volume set constitutes the refereed proceedings of the Second International Conference, ISDS 2024, held in Nha Trang, Vietnam, during November 9–10, 2024. The 38 full papers and 10 short papers were carefully reviewed and selected from 129 submissions. They were categorized under the topical sections as follows: AI in E-Commerce, Agriculture, and Aquaculture; AI in Health Care Analytics; Big Data, IoT, and Cloud Computing; and Natural Language Processing.

The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024. The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation.

The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024. The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation.

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 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 trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

This book constitutes the proceedings of the 8th International Joint Conference on Rules and Reasoning, RuleML+RR 2024, held in Bucharest, Romania, during September 16-18, 2024. The 12 full papers and 4 short papers included in this book were carefully reviewed and selected from 35 submissions. The RuleML+RR symposia were devoted to disseminating research, applications, languages, and standards for rule technologies, with attention to both theoretical and practical developments, to challenging new ideas and to industrial applications.

This book constitutes the proceedings of the 6th International Workshop on Formal Methods Teaching, FMTea 2024, which was held in Milan, Italy, on September 10, 2024. The 7 full papers included in these proceedings were carefully reviewed and selected from 9 submissions. The book also contains one invited talk in full paper length. The papers focus on learning formal methods for the purpose of teaching and self-learning.

Create engaging AI experiences by mastering ChatGPT for business and leveraging user interface design practices, research methods, prompt engineering, the feeding lifecycle, and moreKey FeaturesLearn in-demand design thinking and user research techniques applicable to all conversational AI platformsMeasure the quality and evaluate ChatGPT from a customer's perspective for optimal user experienceSet up and use your secure private data, documents, and materials to enhance your ChatGPT modelsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMany enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach. UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience's voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You'll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions. By the end, you'll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization's use of ChatGPT.What you will learnAlign with user needs by applying design thinking to tailor ChatGPT to meet customer expectationsHarness user research to enhance chatbots and recommendation enginesTrack quality metrics and learn methods to evaluate and monitor ChatGPT's quality and usabilityEstablish and maintain a uniform style and tone with prompt engineering and fine-tuningApply proven heuristics by monitoring and assessing the UX for conversational experiences with trusted methodsRefine continuously by implementing an ongoing process for chatbot and feedingWho this book is forThis book is for user experience designers, product managers, and product owners of business and enterprise ChatGPT solutions who are interested in learning how to design and implement ChatGPT-4 solutions for enterprise needs. You should have a basic-to-intermediate level of understanding in UI/UX design concepts and fundamental knowledge of ChatGPT-4 and its capabilities.

Create a portfolio of cool and creative websites—all without having to write your own code.Build a Website with ChatGPT teaches you zero-coding web development utilizing powerful generative AI tools like ChatGPT. If you can open a web browser, you're ready to start building—absolutely no coding experience required. Inside Build a Website with ChatGPT you'll learn the important skills of AI-assisted web programming, such as: • Crafting effective prompts to generate HTML, CSS, and JavaScript • Converting text into images with DALL-E integration • Building navigation bars, image galleries, and contact forms • Deploying fully functional sites to the web for free • Customizing the generated code for unique sites Inside Build a Website with ChatGPT you'll learn the high-level coding concepts that let you check and perfect AI output, prompting skills that deliver the exact code you need, and how to properly deploy your site to the web—for free! Annotated code samples and advice on code customization give you the perfect balance of understanding and convenience. Plus, you'll get access to a tried-and-tested repository of prompts and working code. About the technology You can build amazing websites even if you don't know HTML, CSS, and JavaScript. Just describe what you want in plain English, and let ChatGPT take care of the gnarly details! This book guides you step-by-step as you create user-friendly forms, interesting graphics, and interactive web pages using nothing but AI and your imagination. About the book Build a Website with ChatGPT shows you how to make websites in an AI-first world—no experience required! You'll start with the basics of generating pages with ChatGPT, and by the end of the second chapter your first site will be up and running. Author Paul McFedries then shows you how to add interesting text and graphics, forms for user input, and even custom CSS to give your pages some pizzazz. As you go, you'll expand your new AI skills to create photo galleries, portfolios, catalog pages and more. What's inside • Writing effective prompts to create code, text, and graphics • Adding navigation bars, image galleries, and contact forms • Deploying your sites to the web for free • Adding your unique touches to AI-generated pages About the reader No experience with web development or programming required. If you can create a Word document, you can build a website! About the author Paul McFedries has written over 100 books on web development and other technology topics including Web Design Playground (Manning Publications). The technical editor on this book was Anirudh V. Prabhu. Table of Contents 1 Introducing website creation with ChatGPT 2 Creating and deploying your first web page 3 Working with fonts, colors, and headings 4 Adding structure to a page 5 Publishing page posts 6 Adding links and navigation 7 Creating site content 8 Generating site forms 9 Adding lists to your pages 10 Setting up a photo gallery 11 Creating a portfolio page 12 Building an article page 13 Coding an interactive course catalog A Getting ready to build web pages with ChatGPT B Deploying your site C Learning a few ChatGPT best practices