ChatGPT and Gemini Chat 101: A Beginner's Guide to Language Models PDF Download
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Author: M.B. Chatfield
Publisher: M.B. Chatfield
ISBN:
Category : Computers
Languages : en
Pages : 163
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Book Description
Embark on a journey to explore the fascinating world of language models, with ChatGPT and Gemini Chat as your guides. In this beginner-friendly guide, you will discover the basics of language models, including their history, development, and applications. You will also learn how to use ChatGPT and Gemini Chat, two powerful language models created by OpenAI and Google AI, respectively. Throughout the guide, you will: Gain a comprehensive understanding of language models and their role in artificial intelligence (AI). Explore the capabilities of ChatGPT and Gemini Chat, including their ability to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. This book is perfect for anyone who wants to learn the basics of language models and how to use them. So what are you waiting for? Start your adventure into the world of language models today! With ChatGPT and Gemini Chat as your guides, you're sure to have an enriching and enlightening experience. Join us as we explore the endless possibilities of language models and their impact on the future.
Author: M.B. Chatfield
Publisher: M.B. Chatfield
ISBN:
Category : Computers
Languages : en
Pages : 163
Get Book Here
Book Description
Embark on a journey to explore the fascinating world of language models, with ChatGPT and Gemini Chat as your guides. In this beginner-friendly guide, you will discover the basics of language models, including their history, development, and applications. You will also learn how to use ChatGPT and Gemini Chat, two powerful language models created by OpenAI and Google AI, respectively. Throughout the guide, you will: Gain a comprehensive understanding of language models and their role in artificial intelligence (AI). Explore the capabilities of ChatGPT and Gemini Chat, including their ability to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. This book is perfect for anyone who wants to learn the basics of language models and how to use them. So what are you waiting for? Start your adventure into the world of language models today! With ChatGPT and Gemini Chat as your guides, you're sure to have an enriching and enlightening experience. Join us as we explore the endless possibilities of language models and their impact on the future.
Author: Anand Vemula
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
"Quick Start Guide to Large Language Models (LLMs)" is a comprehensive manual designed to demystify the complexities of LLMs and equip readers with practical knowledge for leveraging these powerful AI tools. The book serves as an accessible entry point for beginners while providing valuable insights for experienced practitioners looking to deepen their expertise. The guide begins with a thorough introduction to LLMs, explaining their significance, fundamental concepts, and the wide range of applications they support. From enhancing customer service to driving advancements in healthcare, LLMs have become indispensable across various industries. Readers are then guided through the initial setup, including prerequisites, environment configuration, and the installation of necessary tools and libraries. This ensures a smooth start for anyone new to working with LLMs. The core of the book delves into the intricacies of training LLMs. It covers data collection and preparation, emphasizing the importance of high-quality data. The process of selecting the right model is discussed in detail, followed by a step-by-step guide to training, including best practices to optimize performance and prevent common pitfalls. Fine-tuning is highlighted as a crucial step in tailoring pre-trained models to specific tasks. Detailed instructions and practical examples are provided to illustrate the fine-tuning process, enabling readers to achieve optimal results with minimal data. The book also addresses the deployment of LLMs, offering insights into various deployment options, integration with applications, and best practices for monitoring and maintenance. Advanced topics such as transfer learning, handling large datasets, and performance optimization are explored to equip readers with the skills needed to handle complex scenarios. Real-world applications are showcased through case studies and industry-specific use cases, demonstrating the transformative impact of LLMs. The book concludes with a discussion of future trends and common challenges, providing practical solutions and ethical considerations to guide responsible AI development. Whether you're a novice or an expert, "Quick Start Guide to Large Language Models (LLMs)" offers a clear, concise, and practical pathway to mastering the potential of LLMs.
Author: Jon Adams
Publisher: Green Mountain Computing
ISBN:
Category : Computers
Languages : en
Pages : 137
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Book Description
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.
Author: James Chen
Publisher: James Chen
ISBN: 1738908461
Category : Computers
Languages : en
Pages : 300
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Book Description
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
Author: Anand Vemula
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
"LLM from Scratch" is an extensive guide designed to take readers from the basics to advanced concepts of large language models (LLMs). It provides a thorough understanding of the theoretical foundations, practical implementation, and real-world applications of LLMs, catering to both beginners and experienced practitioners. Part I: Foundations The book begins with an introduction to language models, detailing their history, evolution, and wide-ranging applications. It covers essential mathematical and theoretical concepts, including probability, statistics, information theory, and linear algebra. Fundamental machine learning principles are also discussed, setting the stage for more complex topics. The basics of Natural Language Processing (NLP) are introduced, covering text preprocessing, tokenization, embeddings, and common NLP tasks. Part II: Building Blocks This section delves into the core components of deep learning and neural networks. It explains various architectures, such as Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The concept of attention mechanisms, especially self-attention and scaled dot-product attention, is explored, highlighting their importance in modern NLP models. Part III: Transformer Models The book provides a detailed examination of the Transformer architecture, which has revolutionized NLP. It covers the encoder-decoder framework, multi-head attention, and the building blocks of transformers. Practical aspects of training transformers, including data preparation, training techniques, and evaluation metrics, are discussed. Advanced transformer variants like BERT, GPT, and others are also reviewed, showcasing their unique features and applications. Part IV: Practical Implementation Readers are guided through setting up their development environment, including the necessary tools and libraries. Detailed instructions for implementing a simple language model, along with a step-by-step code walkthrough, are provided. Techniques for fine-tuning pre-trained models using transfer learning are explained, supported by case studies and practical examples. Part V: Applications and Future Directions The book concludes with real-world applications of LLMs across various industries, including healthcare, finance, and retail. Ethical considerations and challenges in deploying LLMs are addressed. Advanced topics such as model compression, zero-shot learning, and future research trends are explored, offering insights into the ongoing evolution of language models. "LLM from Scratch" is an indispensable resource for anyone looking to master the intricacies of large language models and leverage their power in practical applications.
Author: Anand Vemula
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
"Large Language Models: A Step-by-Step Do It Yourself Guide" is an essential resource for those looking to understand and develop large language models (LLMs) from scratch. This comprehensive guide takes readers through the entire process, from foundational concepts to advanced techniques, ensuring a thorough understanding of both the theory and practical application of LLMs. The book begins with an introduction to LLMs, covering their definitions, historical evolution, and key concepts. It explores various applications, including natural language processing, conversational AI, and text generation. Ethical considerations, such as bias and privacy, are also addressed, setting the stage for responsible AI development. In the next section, readers are guided through the process of building their own LLMs. This includes setting up the development environment, understanding essential machine learning concepts, and collecting and preparing data. Detailed tutorials on model architecture and design follow, including insights into transformers, attention mechanisms, and custom model design. Training strategies and techniques are discussed, with practical examples of fine-tuning and transfer learning. The book then shifts focus to deployment and practical use. It covers various deployment strategies, integrating LLMs with applications and services, and best practices for monitoring and maintaining models. Hands-on projects such as creating chatbots, text summarization tools, and personalized recommendation systems are included, offering readers real-world experience. Advanced topics, including innovative training methods and case studies, round out the guide. Real-world examples, like implementing customer support bots and automating content generation, provide valuable insights into practical applications of LLMs. Overall, this guide equips readers with the knowledge and skills needed to build, deploy, and optimize their own large language models, making it an indispensable resource for AI enthusiasts and professionals alike.
Author: Sinan Ozdemir
Publisher: Addison-Wesley Professional
ISBN: 0138199337
Category : Computers
Languages : en
Pages : 429
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Book Description
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data Construct and fine-tune multimodal Transformer architectures using opensource LLMs Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Anand Vemula
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
"Quick Start Guide to LLMs: Hands-On with Large Language Models" is a comprehensive yet concise manual designed to equip readers with the knowledge and skills needed to understand and utilize Large Language Models (LLMs). The book delves into the fascinating world of LLMs, exploring their significance, architecture, and practical applications. The introduction sets the stage by explaining what LLMs are and why they are important in today's AI landscape. It provides an overview of the book, outlining the key topics covered in each chapter. Chapter 1, "Understanding the Basics," lays the foundation by discussing the core concepts, history, and evolution of LLMs. It introduces key terminology and explains the fundamental principles that underpin these powerful models. In Chapter 2, "Getting Started with LLMs," readers learn how to set up their environment, including software and hardware requirements. This chapter provides step-by-step instructions for installing necessary tools and libraries, making it easy for beginners to start working with LLMs. Chapter 3, "Core Components and Architecture," takes a deep dive into the internal workings of LLMs. It covers model architecture, training data, preprocessing, and techniques for fine-tuning and customization, offering readers a thorough understanding of how these models operate. Chapter 4, "Hands-On with LLMs," is the heart of the book. It guides readers through basic operations such as text generation, text completion, and summarization. It also explores advanced use cases, including translation, question answering, and building dialogue systems, with practical examples and code snippets. Chapter 5, "Practical Applications," shows how to integrate LLMs into projects with real-world case studies and examples. Readers will learn how to define problems, choose the right models, implement solutions, and deploy applications effectively. In Chapter 6, "Best Practices and Optimization," the book offers strategies for improving performance, managing costs, and ensuring efficient operation. It covers topics like model optimization, resource management, and cost reduction techniques. Chapter 7, "Ethical Considerations," addresses the crucial issues of bias, fairness, and privacy. It provides guidelines for mitigating risks and ensuring ethical use of LLMs. Finally, Chapter 8, "Future Trends and Innovations," looks ahead to the evolving landscape of LLMs. It discusses emerging technologies, industry trends, and the future directions of AI, helping readers stay informed and prepared for what's next. "Quick Start Guide to LLMs: Hands-On with Large Language Models" is an essential resource for anyone looking to harness the power of LLMs, offering practical insights and hands-on experience in building and deploying AI solutions.
Author: Anand Vemula
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
Part 1: The Power of Language LLMs Demystified: Imagine a computer program that can understand and respond to human language like a super-powered assistant. That's the magic of LLMs! Trained on vast amounts of text data, they can translate languages, write different creative formats, and even answer your questions in an informative way. A World of Possibilities: The applications of LLMs are vast. They personalize learning experiences, assist researchers with data analysis, and even help with creative writing. Imagine a future where chatbots become indistinguishable from humans, or a world where language barriers disappear with real-time translation. Part 2: Unveiling the Open-Source Stars The Heavyweights: Meet LLaMA and BLOOM, the powerhouses of open-source LLMs. LLaMA tackles not just text but also understands images and code, making it a versatile tool. BLOOM shines in multilingual processing, understanding and responding in a vast array of languages. Familiar Faces: GPT-J and GPT-NeoX bring the power of GPT technology to the open-source world. GPT-J offers a balance between performance and accessibility, while GPT-NeoX is a powerhouse for those with high-end machines. Specialized Stars: Falcon and BART showcase the diversity of open-source LLMs. Falcon excels at generating creative text formats like poems or scripts, while BART masters understanding complex factual language, perfect for question answering and summarizing information. Part 3: Working with Your LLM Accessing and Running: Whether you have a powerful computer or limited resources, this section equips you with the knowledge to set up your environment. Explore local installations or discover cloud-based solutions to run your chosen LLM. The Art of Prompt Engineering: Unlocking the true potential of LLMs lies in "prompt engineering." Learn to craft clear, specific instructions that guide the LLM towards your desired outcome. By providing context and examples, you'll achieve impressive results. Fine-Tuning for Specificity: Pre-trained models are a great starting point, but fine-tuning takes it further. This process exposes the LLM to data specific to your task, significantly improving its accuracy and performance for specialized applications. This book empowers you to navigate the world of open-source LLMs responsibly. Explore the future of AI, where language models become powerful tools for communication, creativity, and problem-solving.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 41
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Book Description
Demystifying Large Language Models: A Comprehensive Guide" serves as an essential roadmap for navigating the complex terrain of cutting-edge language technologies. In this book, readers are taken on a journey into the heart of Large Language Models (LLMs), exploring their significance, mechanics, and real-world applications. The narrative begins by contextualizing LLMs within the broader landscape of artificial intelligence and natural language processing, offering a clear understanding of their evolution and the pivotal role they play in modern computational linguistics. Delving into the workings of LLMs, the book breaks down intricate concepts into digestible insights, ensuring accessibility for both technical and non-technical audiences. Readers are introduced to the underlying architectures and training methodologies that power LLMs, including Transformer models like GPT (Generative Pre-trained Transformer) series. Through illustrative examples and practical explanations, complex technical details are demystified, empowering readers to grasp the essence of how these models generate human-like text and responses. Beyond theoretical underpinnings, the book explores diverse applications of LLMs across industries and disciplines. From natural language understanding and generation to sentiment analysis and machine translation, readers gain valuable insights into how LLMs are revolutionizing tasks once deemed exclusive to human intelligence. Moreover, the book addresses critical considerations surrounding ethics, bias, and responsible deployment of LLMs in real-world scenarios. It prompts readers to reflect on the societal implications of these technologies and encourages a thoughtful approach towards their development and utilization. With its comprehensive coverage and accessible language, "Demystifying Large Language Models" equips readers with the knowledge and understanding needed to engage with LLMs confidently. Whether you're a researcher, industry professional, or curious enthusiast, this book offers invaluable insights into the present and future of language technology.
