Generative Ai With Large Language Models A Comprehensive Guide
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A Comprehensive Handbook To Llms And Generative Ai
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Author : Dr. Sitanath Biswas
language : en
Publisher: JEC PUBLICATION
Release Date :
A Comprehensive Handbook To Llms And Generative Ai written by Dr. Sitanath Biswas and has been published by JEC PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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Generative Ai And Large Language Models
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Author : Aditya Pratap Bhuyan
language : en
Publisher: Aditya Pratap Bhuyan
Release Date : 2024-07-24
Generative Ai And Large Language Models written by Aditya Pratap Bhuyan and has been published by Aditya Pratap Bhuyan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Computers categories.
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.
Generative Ai With Large Language Models
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Author : Anand Vemula
language : en
Publisher: Independently Published
Release Date : 2024-05-18
Generative Ai With Large Language Models written by Anand Vemula and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-18 with Computers categories.
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 deploym
Generative Ai With Local Llm
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Author : Timur Isachenko
language : en
Publisher: Timur Isachenko
Release Date : 2024-10-04
Generative Ai With Local Llm written by Timur Isachenko and has been published by Timur Isachenko this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-04 with Computers categories.
A comprehensive roadmap for building AI-Driven applications with local LLMs
A Comprehensive Guide To Gen Ai In Healthcare Transformation 2025
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Author : Lakshman Kumar Jamili, Dr. Rahul Kumar
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
A Comprehensive Guide To Gen Ai In Healthcare Transformation 2025 written by Lakshman Kumar Jamili, Dr. Rahul Kumar and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
brink of a technological revolution, driven by the rapid advancements in artificial intelligence. Among the most groundbreaking innovations is Generative AI (Gen AI), a powerful subset of AI that is transforming diagnostics, personalized medicine, clinical workflows, and patient engagement. By leveraging deep learning models, Gen AI is not only enhancing decision-making for healthcare professionals but also improving outcomes, reducing costs, and optimizing operational efficiency. This book, A Comprehensive Guide to Gen AI in Healthcare Transformation, explores how generative AI is reshaping every facet of healthcare. From AI-assisted drug discovery and predictive analytics to automated documentation and virtual health assistants, we delve into the practical applications, benefits, and challenges of this emerging technology. Beyond its technical capabilities, Gen AI also raises critical ethical, regulatory, and privacy concerns. As healthcare institutions adopt AI-driven solutions, questions about data security, bias mitigation, and regulatory compliance become more pressing. This book provides a balanced perspective, addressing both the immense potential and the responsibilities that come with integrating AI into healthcare systems. Our goal is to equip healthcare professionals, researchers, policymakers, and technology leaders with the knowledge and insights needed to harness Gen AI effectively. Whether you are exploring AI’s role in medical research or seeking ways to implement AI-driven solutions in clinical practice, this book serves as a comprehensive guide to navigating the future of healthcare innovation. The transformation is already underway. The question is no longer if AI will redefine healthcare but howwe can maximize its impact responsibly and effectively. Authors
The Complete Obsolete Guide To Generative Ai
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Author : David Clinton
language : en
Publisher: Simon and Schuster
Release Date : 2024-08-27
The Complete Obsolete Guide To Generative Ai written by David Clinton and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Computers categories.
The Complete Obsolete Guide to Generative AI is a lighthearted look at programming with AI, as well as a rock-solid resource for getting the best out of these insanely powerful services. Let it be your guide to creating images and presentations, working with data feeds, prompt engineering, and much more.
Learn Python Generative Ai
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Author : Zonunfeli Ralte
language : en
Publisher: BPB Publications
Release Date : 2024-02-01
Learn Python Generative Ai written by Zonunfeli Ralte and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-01 with Computers categories.
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
Ultimate Generative Ai Solutions On Google Cloud
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Author : Arun Pandey
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-12-29
Ultimate Generative Ai Solutions On Google Cloud written by Arun Pandey and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-29 with Computers categories.
TAGLINE Unlock Generative AI's Potential: Transform Ideas into Reality on Google Cloud! KEY FEATURES ● Step-by-step guidance for building Generative AI apps on Google Cloud Platform. ● Pro tips for fine-tuning models to achieve optimal performance. ● Industry-specific use cases for practical, hands-on learning. DESCRIPTION Generative AI, powered by Google Cloud Platform (GCP), is reshaping industries with its advanced capabilities in automating and enhancing complex tasks. The Ultimate Generative AI Solutions on Google Cloud is your comprehensive guide to harnessing this powerful combination to innovate and excel in your job role. It explores foundational machine learning concepts and dives deep into Generative AI, providing the essential knowledge needed to conceptualize, develop, and deploy cutting-edge AI solutions. Within these pages, you'll explore Large Language Models (LLMs), Prompt engineering, Fine-tuning techniques, and the latest advancements in AI, with special emphasis on Parameter-Efficient Fine-Tuning (PEFT) and Reinforcement Learning with Human Feedback (RLHF). You'll also learn about the integration of LangChain and Retrieval-Augmented Generation (RAG) to enhance AI capabilities. By mastering these techniques, you can optimize model performance while conserving resources. The integration of GCP services simplifies the development process, enabling the creation of robust AI applications with ease. By the end of this book, you will not only understand the technical aspects of Generative AI but also gain practical skills that can transform your work to drive innovation and boost operational efficiency with Generative AI on GCP. WHAT WILL YOU LEARN ● Build and deploy cutting-edge generative AI solutions using Google Cloud, LangChain, and RAG. ● Fine-tune large language models (LLMs) with PEFT to meet precise business objectives. ● Master prompt engineering techniques to enhance model performance with GCP tools. ● Optimize production AI for efficiency and scalability using GCP’s Cloud Functions and Cloud Run. ● Apply real-world industry use cases to drive innovation and solve complex problems with LLMOps. ● Manage and streamline AI projects effectively using GCP services like Dataflow, Pub/Sub, and Monitoring. WHO IS THIS BOOK FOR? This book is tailored for AI practitioners, data scientists, and developers with a foundational knowledge of machine learning and experience using Google Cloud services. Familiarity with Python programming and an interest in generative AI applications will enrich your understanding and support practical implementation of the concepts. TABLE OF CONTENTS 1. Generative AI Essentials 2. Google Cloud Basics 3. Getting Started with Large Language Models 4. Prompt Engineering and Contextual Learning 5. Fine-Tuning a Large Language Model 6. Parameter-Efficient Fine-Tuning (PEFT) 7. Reinforcement Learning with Human Feedback 8. Model Optimization 9. LLMOps for Managing and Monitoring AI Projects 10. Harnessing RAG and LangChain 11. Case Studies and Real-World Implementations Index
Generative Artificial Intelligence In Finance
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Author : Pethuru Raj Chelliah
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-11
Generative Artificial Intelligence In Finance written by Pethuru Raj Chelliah and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-11 with Computers categories.
This comprehensive volume delves deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape. Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management. Generative AI is a key theme across the accounting and finance sectors to drive significant optimizations leading to sustainability. Across 22 chapters, leading researchers supply innovative applications of large language models across the economic realm. Through detailed frameworks, real-world case studies, and governance recommendations, this book highlights applied research for generative AI in finance functions. Several chapters demonstrate how data-driven insights from AI systems can optimize complex financial processes to reduce resource usage, lower costs, and drive positive environmental impact over the long term. In addition, chapters on AI-enabled risk assessment, fraud analytics, and regulatory technology highlight applied research for generative AI in finance. The book also explores emerging applications like leveraging blockchain and metaverse interfaces to create generative AI models that can revolutionize areas from carbon credit trading to virtual audits. Overall, with in-depth applied research at the nexus of sustainability and optimization enabled by data science and generative AI, the book offers a compilation of best practices in leveraging AI for optimal, ethical, and future-oriented financial management. Audience The audience for this book is quite diverse, ranging from financial and accounting experts across banking, insurance, consultancies, regulatory agencies, and corporations seeking to enhance productivity and efficiency; business leaders want to implement ethical and compliant AI practices; researchers exploring the domain of AI and finance.
Mastering Large Language Models With Python Unleash The Power Of Advanced Natural Language Processing For Enterprise Innovation And Efficiency Using Large Language Models Llms With Python
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Author : Raj Arun
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-04-12
Mastering Large Language Models With Python Unleash The Power Of Advanced Natural Language Processing For Enterprise Innovation And Efficiency Using Large Language Models Llms With Python written by Raj Arun and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.
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. Book 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 you will 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. 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