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Language at Large by Alexandra Aikhenvald,R.M.W. Dixon Pdf
The volume brings together important essays on syntax and semantics by Aikhenvald and Dixon. It focusses on topics in linguistic typology, the analysis of previously undescribed languages and issues in the grammar and lexicography of English.
Demystifying Large Language Models by James Chen Pdf
This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models. That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms. Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents 1. INTRODUCTION 1.1 What is AI, ML, DL, Generative AI and Large Language Model 1.2 Lifecycle of Large Language Models 1.3 Whom This Book Is For 1.4 How This Book Is Organized 1.5 Source Code and Resources 2. PYTORCH BASICS AND MATH FUNDAMENTALS 2.1 Tensor and Vector 2.2 Tensor and Matrix 2.3 Dot Product 2.4 Softmax 2.5 Cross Entropy 2.6 GPU Support 2.7 Linear Transformation 2.8 Embedding 2.9 Neural Network 2.10 Bigram and N-gram Models 2.11 Greedy, Random Sampling and Beam 2.12 Rank of Matrices 2.13 Singular Value Decomposition (SVD) 2.14 Conclusion 3. TRANSFORMER 3.1 Dataset and Tokenization 3.2 Embedding 3.3 Positional Encoding 3.4 Layer Normalization 3.5 Feed Forward 3.6 Scaled Dot-Product Attention 3.7 Mask 3.8 Multi-Head Attention 3.9 Encoder Layer and Encoder 3.10 Decoder Layer and Decoder 3.11 Transformer 3.12 Training 3.13 Inference 3.14 Conclusion 4. PRE-TRAINING 4.1 Machine Translation 4.2 Dataset and Tokenization 4.3 Load Data in Batch 4.4 Pre-Training nn.Transformer Model 4.5 Inference 4.6 Popular Large Language Models 4.7 Computational Resources 4.8 Prompt Engineering and In-context Learning (ICL) 4.9 Prompt Engineering on FLAN-T5 4.10 Pipelines 4.11 Conclusion 5. FINE-TUNING 5.1 Fine-Tuning 5.2 Parameter Efficient Fine-tuning (PEFT) 5.3 Low-Rank Adaptation (LoRA) 5.4 Adapter 5.5 Prompt Tuning 5.6 Evaluation 5.7 Reinforcement Learning 5.8 Reinforcement Learning Human Feedback (RLHF) 5.9 Implementation of RLHF 5.10 Conclusion 6. DEPLOYMENT OF LLMS 6.1 Challenges and Considerations 6.2 Pre-Deployment Optimization 6.3 Security and Privacy 6.4 Deployment Architectures 6.5 Scalability and Load Balancing 6.6 Compliance and Ethics Review 6.7 Model Versioning and Updates 6.8 LLM-Powered Applications 6.9 Vector Database 6.10 LangChain 6.11 Chatbot, Example of LLM-Powered Application 6.12 WebUI, Example of LLM-Power Application 6.13 Future Trends and Challenges 6.14 Conclusion REFERENCES ABOUT THE AUTHOR
Mastering Large Language Models by Sanket Subhash Khandare Pdf
Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact
Artificial Intelligence and Large Language Models by Kutub Thakur,Helen G. Barker,Al-Sakib Khan Pathan Pdf
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 practitioners Illustrated 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.
Mastering Large Language Models with Python by Raj Arun R Pdf
A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
Large Language Model-Based Solutions by Shreyas Subramanian Pdf
Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Prompt Engineering for Large Language Models by Nimrita Koul Pdf
This eBook ‘Prompt Engineering for Large Language Models’ is meant to be a concise and practical guide for the reader. It teaches you to write better prompts for generative artificial intelligence models like Google’s BARD and OpenAI’s ChatGPT. These models have been trained on huge volumes of data to generate text and provide a free of cost, web-based interface to the underlying models as of 11 Nov. 2023. These models are fine tuned for conversational AI applications. All the prompts used in the eBook have been tested on the web interface of BARD and ChatGPT-3.5.
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models by Sharma, Avinash Kumar,Chanderwal, Nitin,Prajapati, Amarjeet,Singh, Pancham,Kansal, Mrignainy Pdf
The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.
AI Foundations of Large Language Models by Jon Adams Pdf
Dive into the fascinating world of artificial intelligence with Jon Adams' groundbreaking book, AI Foundations of Large Language Models. This comprehensive guide serves as a beacon for both beginners and enthusiasts eager to understand the intricate mechanisms behind the digital forces shaping our future. With Adams' expert narration, readers are invited to explore the evolution of language models that have transformed mere strings of code into entities capable of human-like text generation. Key Features: In-depth Exploration: From the initial emergence to the sophisticated development of Large Language Models (LLMs), this book covers it all. Technical Insights: Understand the foundational technology, including neural networks, transformers, and attention mechanisms, that powers LLMs. Practical Applications: Discover how LLMs are being utilized in industry and research, paving the way for future innovations. Ethical Considerations: Engage with the critical discussions surrounding the ethics of LLM development and deployment. Chapters Include: The Emergence of Language Models: An introduction to the genesis of LLMs and their significance. Foundations of Neural Networks: Delve into the neural underpinnings that make it all possible. Transformers and Attention Mechanisms: Unpack the mechanisms that enhance LLM efficiency and accuracy. Training Large Language Models: A guide through the complexities of LLM training processes. Understanding LLMs Text Generation: Insights into how LLMs generate text that rivals human writing. Natural Language Understanding: Explore the advancements in LLMs' comprehension capabilities. Ethics and LLMs: A critical look at the ethical landscape of LLM technology. LLMs in Industry and Research: Real-world applications and the impact of LLMs across various sectors. The Future of Large Language Models: Speculations and predictions on the trajectory of LLM advancements. Whether you're a student, professional, or simply an AI enthusiast, AI Foundations of Large Language Models by Jon Adams offers a riveting narrative filled with insights and foresights. Equip yourself with the knowledge to navigate the burgeoning world of LLMs and appreciate their potential to redefine our technological landscape. Join us on this enlightening journey through the annals of artificial intelligence, where the future of digital communication and creativity awaits.
Pretrain Vision and Large Language Models in Python by Emily Webber,Andrea Olgiati Pdf
Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples Key Features Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines Explore large-scale distributed training for models and datasets with AWS and SageMaker examples Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring Book Description Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization. With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you'll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models. You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines. By the end of this book, you'll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future. What you will learn Find the right use cases and datasets for pretraining and fine-tuning Prepare for large-scale training with custom accelerators and GPUs Configure environments on AWS and SageMaker to maximize performance Select hyperparameters based on your model and constraints Distribute your model and dataset using many types of parallelism Avoid pitfalls with job restarts, intermittent health checks, and more Evaluate your model with quantitative and qualitative insights Deploy your models with runtime improvements and monitoring pipelines Who this book is for If you're a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.
Large Language Models ( LLMs) by Maria Johnsen Pdf
This book offers an in-depth exploration of the world of Artificial Intelligence (AI) and Natural Language Processing (NLP), with a special focus on Large Language Models (LLMs). It is designed with academics in mind, making it a perfect resource for students and researchers. Starting with a foundational introduction to AI and its subfields, the book traces the evolution of NLP from rule-based systems to advanced neural networks. It explains the core concepts and architecture of neural networks, highlighting the transformative impact of transformers and attention mechanisms—crucial components for understanding how LLMs process natural language. Detailed explanations of encoder-decoder structures, positional encoding, and various types of neural networks provide a solid technical grounding. A significant portion of the book is dedicated to the practical aspects of working with LLMs. It covers data collection and preprocessing techniques, training objectives, optimization algorithms, and methods for scaling up models. The transition from GPT-2 to GPT-4 is used as a case study to illustrate the computational challenges and advancements in the field. The applications of LLMs are explored across various industries, showcasing their impact on customer service, content creation, journalism, healthcare, and education. Additionally, the book delves into the integration of text with other modalities in multimodal models and the capabilities of zero-shot and few-shot learning. Ethical considerations are a key focus, with discussions on understanding and mitigating bias in LLMs, ensuring responsible AI use, and addressing regulatory and legal implications. The future of LLMs is also contemplated, with predictions for emerging trends and technologies. To provide practical guidance, the book includes chapters on setting up the environment, building and optimizing simple language models, and deploying LLMs in production. It concludes with recommendations for further reading and resources, encouraging continuous learning in this rapidly evolving field. "Large Language Models (LLM)" is a comprehensive resource for anyone interested in understanding, developing, and applying LLMs, from beginners to advanced practitioners. Students are encouraged to buy this book to deepen their knowledge and enhance their academic pursuits.
Natural Language Processing Using Very Large Corpora by S. Armstrong,Kenneth W. Church,Pierre Isabelle,Sandra Manzi,Evelyne Tzoukermann,David Yarowsky Pdf
ABOUT THIS BOOK This book is intended for researchers who want to keep abreast of cur rent developments in corpus-based natural language processing. It is not meant as an introduction to this field; for readers who need one, several entry-level texts are available, including those of (Church and Mercer, 1993; Charniak, 1993; Jelinek, 1997). This book captures the essence of a series of highly successful work shops held in the last few years. The response in 1993 to the initial Workshop on Very Large Corpora (Columbus, Ohio) was so enthusias tic that we were encouraged to make it an annual event. The following year, we staged the Second Workshop on Very Large Corpora in Ky oto. As a way of managing these annual workshops, we then decided to register a special interest group called SIGDAT with the Association for Computational Linguistics. The demand for international forums on corpus-based NLP has been expanding so rapidly that in 1995 SIGDAT was led to organize not only the Third Workshop on Very Large Corpora (Cambridge, Mass. ) but also a complementary workshop entitled From Texts to Tags (Dublin). Obviously, the success of these workshops was in some measure a re flection of the growing popularity of corpus-based methods in the NLP community. But first and foremost, it was due to the fact that the work shops attracted so many high-quality papers.
Programming Large Language Models with Azure Open AI by Francesco Esposito Pdf
Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software
Introduction to Large Language Models for Business Leaders by I. Almeida Pdf
Responsible AI Strategy Beyond Fear and Hype - 2024 Edition Shortlisted for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction. Explore the transformative potential of technologies like GPT-4 and Claude 2. These large language models (LLMs) promise to reshape how businesses operate. Aimed at non-technical business leaders, this guide offers a pragmatic approach to leveraging LLMs for tangible benefits, while ensuring ethical considerations aren't sidelined. LLMs can refine processes in marketing, software development, HR, R&D, customer service, and even legal operations. But it's essential to approach them with a balanced view. In this guide, you'll: - Learn about the rapid advancements of LLMs. - Understand complex concepts in simple terms. - Discover practical business applications. - Get strategies for smooth integration. - Assess potential impacts on your team. - Delve into the ethics of deploying LLMs. With a clear aim to inform rather than influence, this book is your roadmap to adopting LLMs thoughtfully, maximizing benefits, and minimizing risks. Let's move beyond the noise and understand how LLMs can genuinely benefit your business. More Than a Book By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. You can also view, for free, the first module of the self-paced course "AI Fundamentals for Business Leaders," and enjoy video lessons and webinars. No credit card required. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically.
