Generative AI with Large Language Models: A Comprehensive Guide PDF Download

Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 43

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Book Description
This book delves into the fascinating world of Generative AI, exploring the two key technologies driving its advancements: Large Language Models (LLMs) and Foundation Models (FMs). Part 1: Foundations LLMs Demystified: We begin by understanding LLMs, powerful AI models trained on massive amounts of text data. These models can generate human-quality text, translate languages, write different creative formats, and even answer your questions in an informative way. The Rise of FMs: However, LLMs are just a piece of the puzzle. We explore Foundation Models, a broader category encompassing models trained on various data types like images, audio, and even scientific data. These models represent a significant leap forward in AI, offering a more versatile approach to information processing. Part 2: LLMs and Generative AI Applications Training LLMs: We delve into the intricate process of training LLMs, from data acquisition and pre-processing to different training techniques like supervised and unsupervised learning. The chapter also explores challenges like computational resources and data bias, along with best practices for responsible LLM training. Fine-Tuning for Specific Tasks: LLMs can be further specialized for targeted tasks through fine-tuning. We explore how fine-tuning allows LLMs to excel in areas like creative writing, code generation, drug discovery, and even music composition. Part 3: Advanced Topics LLM Architectures: We take a deep dive into the technical aspects of LLMs, exploring the workings of Transformer networks, the backbone of modern LLMs. We also examine the role of attention mechanisms in LLM processing and learn about different prominent LLM architectures like GPT-3 and Jurassic-1 Jumbo. Scaling Generative AI: Scaling up LLMs presents significant computational challenges. The chapter explores techniques like model parallelism and distributed training to address these hurdles, along with hardware considerations like GPUs and TPUs that facilitate efficient LLM training. Most importantly, we discuss the crucial role of safety and ethics in generative AI development. Mitigating bias, addressing potential risks like deepfakes, and ensuring transparency are all essential for responsible AI development. Part 4: The Future Evolving Generative AI Landscape: We explore emerging trends in LLM research, like the development of even larger and more capable models, along with advancements in explainable AI and the rise of multimodal LLMs that can handle different data types. We also discuss the potential applications of generative AI in unforeseen areas like personalized education and healthcare. Societal Impact and the Future of Work: The book concludes by examining the societal and economic implications of generative AI. We explore the potential transformation of industries, the need for workforce reskilling, and the importance of human-AI collaboration. Additionally, the book emphasizes the need for robust regulations to address concerns like bias, data privacy, and transparency in generative AI development. This book equips you with a comprehensive understanding of generative AI, its core technologies, its applications, and the considerations for its responsible development and deployment.

Author: David Foster
Publisher: "O'Reilly Media, Inc."
ISBN: 1492041890
Category : Computers
Languages : en
Pages : 301

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Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Author: Aditya Pratap Bhuyan
Publisher: Aditya Pratap Bhuyan
ISBN:
Category : Computers
Languages : en
Pages : 109

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Book Description
Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.

Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 33

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Book Description
"Generative AI Automation: A Comprehensive Guide" offers a thorough exploration of the burgeoning field of generative artificial intelligence (AI), providing readers with a comprehensive understanding of its principles, applications, and potential impact. This book serves as a roadmap for both beginners and seasoned professionals, breaking down complex concepts into digestible chunks while offering practical insights for implementation. Beginning with an introduction to the fundamentals of AI and machine learning, the book gradually delves into the intricacies of generative models, explaining how they enable machines to create new content, such as images, music, text, and more, autonomously. Readers are guided through various generative AI techniques, including popular models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models, with clear explanations and real-world examples illustrating their capabilities. The book doesn't just stop at theory; it provides hands-on guidance for readers to start experimenting with generative AI themselves. Step-by-step tutorials and coding examples using popular frameworks like TensorFlow and PyTorch empower readers to build their own generative models and understand the practical considerations involved in training and deploying them. Moreover, the book explores the ethical implications and societal impacts of generative AI, addressing concerns such as bias, privacy, and the future of work in an AI-driven world. With its accessible language, practical approach, and comprehensive coverage, "Generative AI Automation: A Comprehensive Guide" equips readers with the knowledge and skills they need to harness the transformative potential of generative AI in their own projects and endeavors. Whether you're a curious novice or an experienced practitioner, this book is your definitive resource for navigating the exciting landscape of generative AI automation.

Author: David A. Joyner
Publisher: MIT Press
ISBN: 026236655X
Category : Education
Languages : en
Pages : 361

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Book Description
A vision of the future of education in which the classroom experience is distributed across space and time without compromising learning. What if there were a model for learning in which the classroom experience was distributed across space and time--and students could still have the benefits of the traditional classroom, even if they can't be present physically or learn synchronously? In this book, two experts in online learning envision a future in which education from kindergarten through graduate school need not be tethered to a single physical classroom. The distributed classroom would neither sacrifice students' social learning experience nor require massive development resources. It goes beyond hybrid learning, so ubiquitous during the COVID-19 pandemic, and MOOCs, so trendy a few years ago, to reimagine the classroom itself. David Joyner and Charles Isbell, both of Georgia Tech, explain how recent developments, including distance learning and learning management systems, have paved the way for the distributed classroom. They propose that we dispense with the dichotomy between online and traditional education, and the assumption that online learning is necessarily inferior. They describe the distributed classroom's various delivery modes for in-person students, remote synchronous students, and remote asynchronous students; the goal would be a symmetry of experiences, with both students and teachers able to move from one mode to another. With The Distributed Classroom, Joyner and Isbell offer an optimistic, learner-centric view of the future of education, in which every person on earth is turned into a potential learner as barriers of cost, geography, and synchronicity disappear.

Author: Andrew Burgess
Publisher: Springer
ISBN: 3319638203
Category : Business & Economics
Languages : en
Pages : 187

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Book Description
This book takes a pragmatic and hype–free approach to explaining artificial intelligence and how it can be utilised by businesses today. At the core of the book is a framework, developed by the author, which describes in non–technical language the eight core capabilities of Artificial Intelligence (AI). Each of these capabilities, ranging from image recognition, through natural language processing, to prediction, is explained using real–life examples and how they can be applied in a business environment. It will include interviews with executives who have successfully implemented AI as well as CEOs from AI vendors and consultancies. AI is one of the most talked about technologies in business today. It has the ability to deliver step–change benefits to organisations and enables forward–thinking CEOs to rethink their business models or create completely new businesses. But most of the real value of AI is hidden behind marketing hyperbole, confusing terminology, inflated expectations and dire warnings of ‘robot overlords’. Any business executive that wants to know how to exploit AI in their business today is left confused and frustrated. As an advisor in Artificial Intelligence, Andrew Burgess regularly comes face–to–face with business executives who are struggling to cut through the hype that surrounds AI. The knowledge and experience he has gained in advising them, as well as working as a strategic advisor to AI vendors and consultancies, has provided him with the skills to help business executives understand what AI is and how they can exploit its many benefits. Through the distilled knowledge included in this book business leaders will be able to take full advantage of this most disruptive of technologies and create substantial competitive advantage for their companies.

Author: David Clinton
Publisher: Simon and Schuster
ISBN: 1633436985
Category : Computers
Languages : en
Pages : 238

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Book Description
The last book on AI you’ll ever need. We swear! AI technology moves so fast that this book is probably already out of date! But don’t worry—The Complete Obsolete Guide to Generative AI is still an essential read for anyone who wants to make generative AI into a tool rather than a toy. It shows you how to get the best out of AI no matter what changes come in the future. You’ll be able to use common automation and scripting tools to take AI to a new level, and access raw (and powerful) GPT models via API. Inside The Complete Obsolete Guide to Generative AI you will find: • Just enough background info on AI! What an AI model is how it works • Ways to create text, code, and images for your organization's needs • Training AI models on your local data stores or on the internet • Business intelligence and analytics uses for AI • Building your own custom AI models • Looking ahead to the future of generative AI Where to get started? How about creating exciting images, video, and even audio with AI. Need more? Learn to harness AI to speed up any everyday work task, including writing boilerplate code, creating specialized documents, and analyzing your own data. Push beyond simple ChatGPT prompts! Discover ways to double your productivity and take on projects you never thought were possible! AI—and this book—are here to show you how. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Everything you learn about Generative AI tools like Chat-GPT, Copilot, and Claude becomes obsolete almost immediately. So how do you decide where to spend your time—and your company’s money? This entertaining and unbelievably practical book shows you what you can (and should!) do with AI now and how to roll with the changes as they happen. About the book The Complete Obsolete Guide to Generative AI is a lighthearted introduction to Generative AI written for technology professionals and motivated AI enthusiasts. In it, you’ll get a quick-paced survey of AI techniques for creating code, text, images, and presentations, working with data, and much more. As you explore the hands-on exercises, you’ll build an intuition for how Generative AI can transform your daily work and communication—and maybe even learn how to make peace with your new robot overlords. What's inside • The big picture of Generative AI tools and tech • Creating useful text, code, and images • Writing effective prompts • AI-driven data analytics About the reader Written for developers, admins, and other IT pros. Some examples use simple Python code. About the author David Clinton is an AWS Solutions Architect, a Linux server administrator and a world-renowned expert on obsolescence. The technical editor on this book was Maris Sekar. Table of Contents 1 Understanding generative AI basics 2 Managing generative AI 3 Creating text and code 4 Creating with media resources 5 Feeding data to your generative AI models 6 Prompt engineering: Optimizing your experience 7 Outperforming legacy research and learning tools 8 Understanding stuff better 9 Building and running your own large language model 10 How I learned to stop worrying and love the chaos 11 Experts weigh in on putting AI to work A Important definitions and a brief history B Generative AI resources C Installing Python

Author: Rajib Kumar De
Publisher: Orange Education Pvt Ltd
ISBN: 8197256225
Category : Computers
Languages : en
Pages : 380

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Book Description
TAGLINE Empower Your Data Science Journey: From Exploration to Certification in Azure Machine Learning KEY FEATURES ● Offers deep dives into key areas such as data preparation, model training, and deployment, ensuring you master each concept. ● Covers all exam objectives in detail, ensuring a thorough understanding of each topic required for the DP-100 certification. ● Includes hands-on labs and practical examples to help you apply theoretical knowledge to real-world scenarios, enhancing your learning experience. DESCRIPTION Ultimate Azure Data Scientist Associate (DP-100) Certification Guide is your essential resource for achieving the Microsoft Azure Data Scientist Associate certification. This guide covers all exam objectives, helping you design and prepare machine learning solutions, explore data, train models, and manage deployment and retraining processes. The book starts with the basics and advances through hands-on exercises and real-world projects, to help you gain practical experience with Azure's tools and services. The book features certification-oriented Q&A challenges that mirror the actual exam, with detailed explanations to help you thoroughly grasp each topic. Perfect for aspiring data scientists, IT professionals, and analysts, this comprehensive guide equips you with the expertise to excel in the DP-100 exam and advance your data science career. WHAT WILL YOU LEARN ● Design and prepare effective machine learning solutions in Microsoft Azure. ● Learn to develop complete machine learning training pipelines, with or without code. ● Explore data, train models, and validate ML pipelines efficiently. ● Deploy, manage, and optimize machine learning models in Azure. ● Utilize Azure's suite of data science tools and services, including Prompt Flow, Model Catalog, and AI Studio. ● Apply real-world data science techniques to business problems. ● Confidently tackle DP-100 certification exam questions and scenarios. WHO IS THIS BOOK FOR? This book is for aspiring Data Scientists, IT Professionals, Developers, Data Analysts, Students, and Business Professionals aiming to Master Azure Data Science. Prior knowledge of basic Data Science concepts and programming, particularly in Python, will be beneficial for making the most of this comprehensive guide. TABLE OF CONTENTS 1. Introduction to Data Science and Azure 2. Setting Up Your Azure Environment 3. Data Ingestion and Storage in Azure 4. Data Transformation and Cleaning 5. Introduction to Machine Learning 6. Azure Machine Learning Studio 7. Model Deployment and Monitoring 8. Embracing AI Revolution Azure 9. Responsible AI and Ethics 10. Big Data Analytics with Azure 11. Real-World Applications and Case Studies 12. Conclusion and Next Steps Index

Author: Zonunfeli Ralte
Publisher: BPB Publications
ISBN: 9355518978
Category : Computers
Languages : en
Pages : 421

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Book Description
Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries. Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Navigate challenges in healthcare, retail, and finance using sector specific insights. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting edge techniques in generative AI. WHO THIS BOOK IS FOR This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI. TABLE OF CONTENTS 1. Introducing Generative AI 2. Designing Generative Adversarial Networks 3. Training and Developing Generative Adversarial Networks 4. Architecting Auto Encoder for Generative AI 5. Building and Training Generative Autoencoders 6. Designing Generative Variation Auto Encoder 7. Building Variational Autoencoders for Generative AI 8. Fundamental of Designing New Age Generative Vision Transformer 9. Implementing Generative Vision Transformer 10. Architectural Refactoring for Generative Modeling 11. Major Technical Roadblocks in Generative AI and Way Forward 12. Overview and Application of Generative AI Models 13. Key Learnings

Author: Arinushkina, Anna A.
Publisher: IGI Global
ISBN:
Category : Education
Languages : en
Pages : 400

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Book Description
Amidst the rapid evolution of educational technology, a pressing challenge confronts higher education institutions: how to effectively integrate generative artificial intelligence (AI) into their existing frameworks. As universities strive to adapt to the digital age, they are met with the complexities of incorporating AI-driven solutions to enhance teaching, learning, and administrative processes. However, the lack of comprehensive strategies and guidance hinders their ability to leverage AI's full potential, leaving educators and administrators grappling with uncertainty. In response to this critical dilemma, Integration Strategies of Generative AI in Higher Education emerges as a guide for clarity and innovation. By offering methodological insights and practical frameworks, this book equips higher education stakeholders with the tools needed to navigate the intricacies of AI integration. From curriculum enhancement to AI-driven content creation, the book provides actionable strategies tailored to the unique needs and challenges of higher education institutions.