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A Deep Dive into Large Language Models- Exploring the Power of Bloom, Vicuna, PaLM, Cohere, Falcon 40B, and Beyond

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 31 pages
Book Rating : 4./5 ( download)

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Book Synopsis A Deep Dive into Large Language Models- Exploring the Power of Bloom, Vicuna, PaLM, Cohere, Falcon 40B, and Beyond by : Anand Vemula

Download or read book A Deep Dive into Large Language Models- Exploring the Power of Bloom, Vicuna, PaLM, Cohere, Falcon 40B, and Beyond written by Anand Vemula and published by Anand Vemula. This book was released on with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Deep Dive into Large Language Models: Unveiling the Power of AI's New Storytellers Unleashing the Power of Language: A New Era of AI Large language models (LLMs) are revolutionizing the way we interact with machines. These AI marvels, trained on massive amounts of text data, can not only understand human language but also generate creative text formats, translate languages, write different kinds of creative content, and answer your questions in an informative way. This book delves into the fascinating world of LLMs, exploring their inner workings, potential applications, and the exciting future they hold. Part I: Demystifying the LLM Landscape We begin by unveiling the core concepts of LLMs. You'll discover how they learn through massive datasets and pre-training, and how the powerful transformer architecture allows them to analyze the nuances of language. We'll also explore the benefits and limitations of LLMs, discussing their potential to automate tasks, enhance creativity, and break down language barriers, while acknowledging concerns about bias and ethical considerations. Part II: Unveiling the Champions: A Look at Pioneering LLM Technologies Get ready to meet the champions of the LLM world! We'll take a deep dive into specific technologies like Bloom (Google AI) with its massive parameter count, Vicuna (Meta AI) excelling in multilingual capabilities, and PaLM (Google AI) boasting a unique pathway system that leverages information beyond just text. We'll also explore Cohere's focus on interpretability and Falcon 40B's (Tsinghua University) strength in factual language understanding. Part III: Charting the Course: The Future of LLMs and Their Impact The journey doesn't end there. We'll explore emerging trends shaping the future of LLMs, like the focus on interpretability, the exciting possibilities of multimodal learning, and the drive for smaller, more efficient models. We'll also delve into the ethical considerations surrounding bias, transparency, and responsible AI practices that are crucial for harnessing the potential of LLMs for good. Finally, we'll examine the profound impact LLMs could have on society, from enhancing automation and personalized experiences to fostering communication and new forms of creativity. This book is your guide to understanding large language models, their capabilities, and the transformative potential they hold for the future. As we move forward, this exploration equips you to be an informed participant in the exciting world of AI language technologies.

LLM Engineer's Handbook

Author : Paul Iusztin
Publisher : Packt Publishing Ltd
ISBN 13 : 1836200064
Total Pages : 523 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis LLM Engineer's Handbook by : Paul Iusztin

Download or read book LLM Engineer's Handbook written by Paul Iusztin and published by Packt Publishing Ltd. This book was released on 2024-10-22 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Key Features Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications Book DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine it with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Design scalable and modularLLM systems Learn about RAG applications by building a feature and inference pipeline Who this book is for This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios

Essential Guide to LLMOps

Author : RYAN. DOAN
Publisher : Packt Publishing Ltd
ISBN 13 : 1835887511
Total Pages : 190 pages
Book Rating : 4.8/5 (358 download)

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Book Synopsis Essential Guide to LLMOps by : RYAN. DOAN

Download or read book Essential Guide to LLMOps written by RYAN. DOAN and published by Packt Publishing Ltd. This book was released on 2024-07-31 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finish Key Features Gain a comprehensive understanding of LLMOps, from data handling to model governance Leverage tools for efficient LLM lifecycle management, from development to maintenance Discover real-world examples of industry cutting-edge trends in generative AI operation Purchase of the print or Kindle book includes a free PDF eBook Book Description The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications. This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workﬂows, enhance eﬃciency in LLMOps processes, employ LLMOps tools for precise model ﬁne-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications. By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI. What you will learn Understand the evolution and impact of LLMs in AI Differentiate between LLMOps and traditional MLOps Utilize LLMOps tools for data analysis, preparation, and fine-tuning Master strategies for model development, deployment, and improvement Implement techniques for model inference, serving, and scalability Integrate human-in-the-loop strategies for refining LLM outputs Grasp the forefront of emerging technologies and practices in LLMOps Who this book is for This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.

Mastering Large Language Models with Python

Author : Raj Arun R
Publisher : Orange Education Pvt Ltd
ISBN 13 : 8197081824
Total Pages : 547 pages
Book Rating : 4.1/5 (97 download)

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Book Synopsis Mastering Large Language Models with Python by : Raj Arun R

Download or read book Mastering Large Language Models with Python written by Raj Arun R and published by Orange Education Pvt Ltd. This book was released on 2024-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Decoding Large Language Models

Author : Irena Cronin
Publisher : Packt Publishing Ltd
ISBN 13 : 1835081800
Total Pages : 396 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Decoding Large Language Models by : Irena Cronin

Download or read book Decoding Large Language Models written by Irena Cronin and published by Packt Publishing Ltd. This book was released on 2024-10-31 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the architecture, development, and deployment strategies of large language models to unlock their full potential Key Features Gain in-depth insight into LLMs, from architecture through to deployment Learn through practical insights into real-world case studies and optimization techniques Get a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challenges Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.What you will learn Explore the architecture and components of contemporary LLMs Examine how LLMs reach decisions and navigate their decision-making process Implement and oversee LLMs effectively within your organization Master dataset preparation and the training process for LLMs Hone your skills in fine-tuning LLMs for targeted NLP tasks Formulate strategies for the thorough testing and evaluation of LLMs Discover the challenges associated with deploying LLMs in production environments Develop effective strategies for integrating LLMs into existing systems Who this book is for If you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.

Demystifying Large Language Models

Author : James Chen
Publisher : James Chen
ISBN 13 : 1738908461
Total Pages : 300 pages
Book Rating : 4.7/5 (389 download)

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Book Synopsis Demystifying Large Language Models by : James Chen

Download or read book Demystifying Large Language Models written by James Chen and published by James Chen. This book was released on 2024-04-25 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Machine Learning with PyTorch and Scikit-Learn

Author : Sebastian Raschka
Publisher : Packt Publishing Ltd
ISBN 13 : 1801816387
Total Pages : 775 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka

Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

The Developer's Playbook for Large Language Model Security

Author : Steve Wilson
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109816217X
Total Pages : 200 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis The Developer's Playbook for Large Language Model Security by : Steve Wilson

Download or read book The Developer's Playbook for Large Language Model Security written by Steve Wilson and published by "O'Reilly Media, Inc.". This book was released on 2024-09-03 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization

Large Language Models Agents Handbook

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 40 pages
Book Rating : 4./5 ( download)

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Book Synopsis Large Language Models Agents Handbook by : Anand Vemula

Download or read book Large Language Models Agents Handbook written by Anand Vemula and published by Anand Vemula. This book was released on with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: The "Large Language Models Agent's Handbook" serves as a comprehensive guide for utilizing large language models (LLMs) effectively. These models, such as GPT-3, have revolutionized natural language processing and are invaluable tools in various fields, including research, business, and creative endeavors. The handbook begins by elucidating the fundamental principles underlying LLMs, explaining their architecture, training process, and capabilities. It delves into the importance of data quality, model fine-tuning, and ethical considerations in deploying LLMs responsibly. Understanding the applications of LLMs is crucial, and the handbook provides detailed insights into their diverse uses. From generating text and code to aiding in decision-making processes, LLMs can augment human capabilities across industries. Case studies showcase real-world examples, illustrating how LLMs have been leveraged for tasks such as content creation, customer service automation, and scientific research. Ethical guidelines are paramount when employing LLMs, and the handbook emphasizes the ethical implications of LLM usage. Issues such as bias, misinformation, and privacy concerns are addressed, alongside strategies for mitigating these risks. Responsible AI practices, including transparency, fairness, and accountability, are advocated throughout. Practical considerations for working with LLMs are explored in detail, covering topics such as model selection, data preprocessing, and performance evaluation. Tips for optimizing model performance and troubleshooting common challenges are provided, empowering users to navigate the complexities of LLM implementation effectively. As LLMs continue to evolve, staying updated with the latest advancements and best practices is essential. The handbook offers resources for ongoing learning, including research papers, online communities, and development tools. Additionally, it encourages collaboration and knowledge sharing among LLM practitioners to foster innovation and collective growth. In conclusion, the "Large Language Models Agent's Handbook" equips readers with the knowledge and tools needed to harness the full potential of LLMs responsibly and effectively. By embracing ethical principles, staying informed about emerging trends, and leveraging practical strategies, agents can leverage LLMs to tackle complex challenges and drive meaningful progress in their respective domains

Generative AI and LLMs

Author : S. Balasubramaniam
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3111425517
Total Pages : 366 pages
Book Rating : 4.1/5 (114 download)

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Book Synopsis Generative AI and LLMs by : S. Balasubramaniam

Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Understanding European Union Law

Author : Karen Davies
Publisher : Taylor & Francis
ISBN 13 : 1000790827
Total Pages : 185 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Understanding European Union Law by : Karen Davies

Download or read book Understanding European Union Law written by Karen Davies and published by Taylor & Francis. This book was released on 2022-12-30 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing short, clear and accessible explanations of the main areas of EU law, Understanding European Union Law is both an ideal introduction for students new to EU law and an essential addition to revision for the more accomplished. This eighth edition has been fully revised and updated with the latest legislative changes and includes an in-depth discussion of ‘Brexit’ and its implications for EU–UK relations. The book provides readers with a clear understanding of the structures and rationale behind EU law, explaining how and why the law has developed as it has. In addition to discussing the core areas of EU law such as its sources, the role and powers of the EU’s Institutions, the enforcement of EU law and the law of the internal market, this edition also includes a new chapter on three ‘non-economic’ areas of EU law: fundamental human rights, equality (non-discrimination) and the environment. This student-friendly text is both broad in scope and highly accessible. It will inspire students towards further study and show that understanding EU law can be an enjoyable and rewarding experience. As well as being essential reading for Law students, Understanding European Union Law is also suitable for students on other courses where basic knowledge of EU law is required or useful, such as business studies, political science, international relations or European studies programmes.

Metamodeling: Applications and Trajectories to the Future

Author : Hans-Georg Fill
Publisher : Springer Nature
ISBN 13 : 3031568621
Total Pages : 225 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Metamodeling: Applications and Trajectories to the Future by : Hans-Georg Fill

Download or read book Metamodeling: Applications and Trajectories to the Future written by Hans-Georg Fill and published by Springer Nature. This book was released on with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Anna's AI Anthology

Author : Daniel Dennett
Publisher : BoD – Books on Demand
ISBN 13 : 3942106981
Total Pages : 380 pages
Book Rating : 4.9/5 (421 download)

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Book Synopsis Anna's AI Anthology by : Daniel Dennett

Download or read book Anna's AI Anthology written by Daniel Dennett and published by BoD – Books on Demand. This book was released on 2024-09-12 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the release of ChatGPT, large language models (LLMs) have become a prominent topic of international public and scientific debate. The genie is out of the bottle, but does it have a mind? Can philosophical considerations help us to work out how we can live with such smart machines? In this book, distinguished philosophers explore questions such as whether these new machines are able to act, whether they are social agents, whether they have communicative skills, and if they might even become conscious. The book includes contributions from Syed AbuMusab, Constant Bonard, Stephen Butterfill, Daniel Dennett, Paula Droege, Keith Frankish, Frederic Gilbert, Ying-Tung Lin, Sven Nyholm, Joshua Rust, Eric Schwitzgebel, Henry Shevlin, Anna Strasser, Alessio Tacca, Michael Wilby, and a graphic novel by Anna and Moritz Strasser as a bonus

Value Engineering in Artificial Intelligence

Author : Nardine Osman
Publisher : Springer Nature
ISBN 13 : 3031582020
Total Pages : 272 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Value Engineering in Artificial Intelligence by : Nardine Osman

Download or read book Value Engineering in Artificial Intelligence written by Nardine Osman and published by Springer Nature. This book was released on with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Business Process Management

Author : Andrea Marrella
Publisher : Springer Nature
ISBN 13 : 3031703960
Total Pages : 575 pages
Book Rating : 4.0/5 (317 download)

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Book Synopsis Business Process Management by : Andrea Marrella

Download or read book Business Process Management written by Andrea Marrella and published by Springer Nature. This book was released on with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt:

The Deep Learning Architect's Handbook

Author : Ee Kin Chin
Publisher : Packt Publishing Ltd
ISBN 13 : 1803235349
Total Pages : 516 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis The Deep Learning Architect's Handbook by : Ee Kin Chin

Download or read book The Deep Learning Architect's Handbook written by Ee Kin Chin and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

1337 Use Cases for ChatGPT & other Chatbots in the AI-Driven Era

Author : Florin Badita
Publisher : Florin Badita
ISBN 13 :
Total Pages : 801 pages
Book Rating : 4./5 ( download)

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Book Synopsis 1337 Use Cases for ChatGPT & other Chatbots in the AI-Driven Era by : Florin Badita

Download or read book 1337 Use Cases for ChatGPT & other Chatbots in the AI-Driven Era written by Florin Badita and published by Florin Badita. This book was released on 2023-01-03 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: "1337 Use Cases for ChatGPT & other Chatbots in the AI-Driven Era" is a book written by Florin Badita that explores the potential uses of advanced large language models (LLMs) like ChatGPT in various industries and scenarios. The book provides 1337 use cases and around 4000 examples of how these technologies can be applied in the future. The author, Florin Badita, is a data scientist, social entrepreneur, activist, and artist who has written about his experiences with data analysis on Medium. He is on the Forbes 30 under 30 list, a TedX speaker, and Landecker Democracy Fellow 2021-2022. He is known for his work in activism, founding the civic group Corruption Kills in 2015, GIS, data analysis, and data mining. The book covers a variety of tips and strategies, including how to avoid errors when converting between different units, how to provide context and examples to improve the LLM's understanding of the content, and how to use the Markdown language to format and style text in chatbot responses. The book is intended for anyone interested in learning more about the capabilities and potential uses of ChatGPT and other language models in the rapidly evolving world of artificial intelligence. After the introduction part and the Table of content, the book is split into 20 categories, each category then being split into smaller categories with at least one use-case and multiple examples A real example from the book: Category: 4 Science and technology [...] Sub-Category: 4.60 Robotics 4.60.1 Text Generation General example text prompt: "Generate a description of a new robot design" Formula: "Generate [description] of [robot design]" Specific examples of prompts: "Generate a detailed description of a robot designed for underwater exploration" "Generate a brief overview of a robot designed for assisting with construction tasks" "Generate a marketing pitch for a robot designed to assist with household chores" 4.60.2 Programming Assistance General example text prompt: "Write code to implement a specific behavior in a robot" Formula: "Write code to [implement behavior] in [robot]" Specific examples of prompts: "Write code to make a robot follow a specific path using sensors and control algorithms" "Write code to make a robot respond to voice commands using natural language processing" "Write code to make a robot perform basic tasks in a manufacturing setting, such as moving objects from one location to another"