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Generative Ai And Large Language Models

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Generative Ai And Llms

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Author : S. Balasubramaniam
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-09-23

Generative Ai And Llms written by S. Balasubramaniam 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 2024-09-23 with Computers categories.

Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

Artificial Intelligence And Large Language Models

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Author : Kutub Thakur
language : en
Publisher: CRC Press
Release Date : 2024-07-12

Artificial Intelligence And Large Language Models written by Kutub Thakur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.

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.

Generative Ai And Llms

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Author : S. Balasubramaniam
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-09-23

Large Language Models Projects

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Author : Pere Martra Manonelles
language : en
Publisher: Apress
Release Date : 2024-10-20

Large Language Models Projects written by Pere Martra Manonelles and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-20 with Computers categories.

This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks

The Generative Ai Practitioner S Guide

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Author : Arup Das
language : en
Publisher: TinyTechMedia LLC
Release Date : 2024-07-20

The Generative Ai Practitioner S Guide written by Arup Das and has been published by TinyTechMedia LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-20 with Computers categories.

Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of? This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI. In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries. Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!™

Demystifying Large Language Models

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Author : James Chen
language : en
Publisher: James Chen
Release Date : 2024-04-25

Demystifying Large Language Models written by James Chen and has been published by James Chen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-25 with Computers categories.

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

Recent Trends Techniques And Applications Of Ai Ml And Iot

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Author : Arif Deen
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-01-06

Recent Trends Techniques And Applications Of Ai Ml And Iot written by Arif Deen and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Business & Economics categories.

Arif Deen, Senior lecturer, Department of Management Studies, Middle East College, Muscat, Oman. Dr.Asad Ullah, Assistant Professor, Department of Management Studies, Middle East College, Muscat, Oman. Dr.Naim Ayadi, Senior Lecturer, Department of Management Studies, Middle East College, Muscat, Oman.

Artificial Intelligence And Games

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Author : Georgios N. Yannakakis
language : en
Publisher: Springer
Release Date : 2018-02-17

Artificial Intelligence And Games written by Georgios N. Yannakakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-17 with Computers categories.

This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.

How Large Language Models Work

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Author : Edward Raff
language : en
Publisher: Simon and Schuster
Release Date : 2025-08-05

How Large Language Models Work written by Edward Raff and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-05 with Computers categories.

Learn how large language models like GPT and Gemini work under the hood in plain English. How Large Language Models Work translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine-tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free. In How Large Language Models Work you will learn how to: • Test and evaluate LLMs • Use human feedback, supervised fine-tuning, and Retrieval Augmented Generation (RAG) • Reducing the risk of bad outputs, high-stakes errors, and automation bias • Human-computer interaction systems • Combine LLMs with traditional ML How Large Language Models Work is authored by top machine learning researchers at Booz Allen Hamilton, including researcher Stella Biderman, Director of AI/ML Research Drew Farris, and Director of Emerging AI Edward Raff. They lay out how LLM and GPT technology works in plain language that’s accessible and engaging for all. About the Technology Large Language Models put the “I” in “AI.” By connecting words, concepts, and patterns from billions of documents, LLMs are able to generate the human-like responses we’ve come to expect from tools like ChatGPT, Claude, and Deep-Seek. In this informative and entertaining book, the world’s best machine learning researchers from Booz Allen Hamilton explore foundational concepts of LLMs, their opportunities and limitations, and the best practices for incorporating AI into your organizations and applications. About the Book How Large Language Models Work takes you inside an LLM, showing step-by-step how a natural language prompt becomes a clear, readable text completion. Written in plain language, you’ll learn how LLMs are created, why they make errors, and how you can design reliable AI solutions. Along the way, you’ll learn how LLMs “think,” how to design LLM-powered applications like agents and Q&A systems, and how to navigate the ethical, legal, and security issues. What’s Inside • Customize LLMs for specific applications • Reduce the risk of bad outputs and bias • Dispel myths about LLMs • Go beyond language processing About the Readers No knowledge of ML or AI systems is required. About the Author Edward Raff, Drew Farris and Stella Biderman are the Director of Emerging AI, Director of AI/ML Research, and machine learning researcher at Booz Allen Hamilton. Table of Contents 1 Big picture: What are LLMs? 2 Tokenizers: How large language models see the world 3 Transformers: How inputs become outputs 4 How LLMs learn 5 How do we constrain the behavior of LLMs? 6 Beyond natural language processing 7 Misconceptions, limits, and eminent abilities of LLMs 8 Designing solutions with large language models 9 Ethics of building and using LLMs Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Challenges And Applications Of Generative Large Language Models

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Author : Anitha S. Pillai
language : en
Publisher: Morgan Kaufmann
Release Date : 2026-01-01

Challenges And Applications Of Generative Large Language Models written by Anitha S. Pillai and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-01 with Computers categories.

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.• Provides a clear and objective description of LLMs, with their strengths and weaknesses.• Demonstrates current applications of LLMs, along with strengths and known issues in each application.• Covers not only the advantages but also risks that LLMs bring today, enabling readers to understand whether a particular LLM fits the problem at hand.

Generative Ai And Large Language Models

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