Adaptive Ai Exploring Fine Tuning And Few Shot Learning In Language Models
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Adaptive Ai Exploring Fine Tuning And Few Shot Learning In Language Models
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Adaptive Ai Exploring Fine Tuning And Few Shot Learning In Language Models written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Adaptive AI" delves into the transformative capabilities of large language models (LLMs) and the critical techniques of fine-tuning and few-shot learning that enhance their adaptability across various applications. The book provides a comprehensive overview of LLMs, tracing their evolution from early models like GPT-1 to the sophisticated architectures of GPT-4 and beyond. The introduction emphasizes the significance of customization in deploying LLMs effectively, highlighting how organizations can leverage these models to meet specific needs, improve user experiences, and drive innovation. The book then defines key concepts, explaining the distinction between fine-tuning—where a pre-trained model is further trained on a domain-specific dataset—and few-shot learning, which enables models to generalize from minimal examples. Throughout the chapters, "Adaptive AI" presents practical insights into implementing fine-tuning and few-shot learning. It covers use cases across diverse sectors, including healthcare, finance, and customer support, illustrating how fine-tuning can enhance language models' understanding of specialized vocabulary and context. Conversely, the few-shot learning section showcases its utility in scenarios with limited data, demonstrating how LLMs can perform effectively even when trained on just a few examples. The book also explores hybrid approaches, combining both fine-tuning and few-shot learning to maximize model performance. It discusses methodologies for evaluating model effectiveness and addresses the ethical considerations and challenges associated with deploying these technologies. In conclusion, "Adaptive AI" serves as a vital resource for AI practitioners, researchers, and industry professionals seeking to harness the full potential of large language models. By providing actionable strategies and real-world case studies, the book equips readers with the knowledge to effectively customize LLMs for diverse applications, paving the way for innovation in AI-driven solutions.
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.
Mastering Spring Ai
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Author : Banu Parasuraman
language : en
Publisher: Springer Nature
Release Date : 2024-12-01
Mastering Spring Ai written by Banu Parasuraman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.
Dive into the future of programming with this comprehensive guide for Java developers to integrate large language models (LLMs) and Generative AI using the Spring Framework. This book comes at a revolutionary time when AI technology is transforming how we implement solutions in various fields, including natural language processing, content generation, and predictive analytics. With its widespread use in the Java community, the Spring Framework is a logical choice for this integration. By focusing on integrating LLMs and GenAI with Spring, this book bridges a significant gap between cutting-edge AI technologies and traditional Java development practices. The author uses a hands-on approach, guiding you through practical implementation to effectively show how to apply theory in real-world situations. Basic introductions of topics—Spring AI, Spring Framework, and other related AI technologies—evolve into advanced integrations to ensure that you find valuable insights regardless of your starting level. Additionally, this book dedicates sections to security and ethical considerations, addressing the pressing issues associated with AI. With a look at emerging trends and future developments, this book prepares you for what's next, ensuring that you are not just catching up with the current state of technology but are also ready for future advancements. What You Will Learn • Master the integration of LLMs and GenAI with the Spring Framework • Develop practical skills in developing AI-driven applications using Java • Gain insights into handling data, security, and ethical considerations in AI applications • Apply strategies for optimizing performance and scalability in AI-enabled applications • Prepare for future AI trends and technologies Who This Book Is For Intermediate to advanced Java developers who are familiar with the Spring Framework, including concepts such as dependency injection, Spring Boot, and building RESTful services. This foundational knowledge will help developers grasp the more advanced topics of integrating AI technologies with Spring. Prior knowledge of basic AI concepts and machine learning is helpful but not essential as the book covers these topics from the ground up.
Generative Ai Application Integration Patterns
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Author : Juan Pablo Bustos
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-05
Generative Ai Application Integration Patterns written by Juan Pablo Bustos and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-05 with Computers categories.
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations. Key Features Get familiar with the most important tools and concepts used in real scenarios to design GenAI apps Interact with GenAI models to tailor model behavior to minimize hallucinations Get acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applications Book Description Explore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI. With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns. We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought. Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns. What you will learn Concepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAG Framework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentation Patterns for batch and real-time integration Code samples for metadata extraction, summarization, intent classification, question-answering with RAG, and more Ethical use: bias mitigation, data privacy, and monitoring Deployment and hosting options for GenAI models Who this book is for This book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include: Developer engineers with foundational tech knowledge Software architects seeking best practices and design patterns Professionals using ML for data science, research, etc., who want a deeper understanding of Generative AI Technical product managers with a software development background This concise focus ensures practical, actionable insights for experienced professionals
Mastering Large Language Models With Python
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Author : Raj Arun R
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-04-12
Mastering Large Language Models With Python written by Raj Arun R 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 2024-04-12 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. ● 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
Generative Ai Foundations In Python
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Author : Carlos Rodriguez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26
Generative Ai Foundations In Python written by Carlos Rodriguez and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Computers categories.
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
Machine Learning And Generative Ai For Marketing
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Author : Yoon Hyup Hwang
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-30
Machine Learning And Generative Ai For Marketing written by Yoon Hyup Hwang and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
Start transforming your data-driven marketing strategies and increasing customer engagement. Learn how to create compelling marketing content using advanced gen AI techniques and stay in touch with the future AI ML landscape. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques Combine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challenges Understand cutting-edge AI concepts and their responsible use in marketing Book Description In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge. What you will learn Master key marketing KPIs with advanced computational techniques Use explanatory data analysis to drive marketing decisions Leverage ML models to predict customer behaviors, engagement levels, and customer lifetime value Enhance customer segmentation with ML and develop highly personalized marketing campaigns Design and execute effective A/B tests to optimize your marketing decisions Apply natural language processing (NLP) to analyze customer feedback and sentiments Integrate ethical AI practices to maintain privacy in data-driven marketing strategies Who this book is for This book targets a diverse group of professionals: Data scientists and analysts in the marketing domain looking to apply advanced AI ML techniques to solve real-world marketing challenges Machine learning engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposes Marketing professionals, business leaders, and entrepreneurs who must understand the impact of AI on marketing Reader are presumed to have a foundational proficiency in Python and a basic to intermediate grasp of ML principles and data science methodologies.
Data Science Exploration In Artificial Intelligence
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Author : Gururaj H L
language : en
Publisher: CRC Press
Release Date : 2025-02-26
Data Science Exploration In Artificial Intelligence written by Gururaj H L and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-26 with Computers categories.
The book captures the essence of the International Conference on Data Science & Exploration in Artificial Intelligence and offers a comprehensive exploration of cutting-edge research in AI, data science, and their applications. It covers a wide array of topics including advanced Data Science, IoT, Security, Cloud Computing, Networks, Security, Image, Video and Signal Processing, Computational Biology, Computer and Information Technology. It highlights innovative research contributions and practical applications, offering readers a detailed understanding of current trends and challenges. The findings emphasize the role of global collaboration and interdisciplinary approaches in pushing the boundaries of AI and data science. Selected papers published by Taylor and Francis showcase pioneering work that is shaping the future of these fields. This is an ideal read for AI and data science researchers, industry professionals, and students seeking to stay updated on the latest advancements and ethical considerations in these areas.
Establishing Ai Specific Cloud Computing Infrastructure
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2025-04-08
Establishing Ai Specific Cloud Computing Infrastructure written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Computers categories.
As artificial intelligence (AI) continues to drive innovation across industries, the need for specialized cloud computing infrastructure to support AI workloads is critical. Traditional cloud platforms often struggle to meet the high computational demands and storage requirements of AI models, especially as they grow in complexity and scale. Establishing AI-specific cloud computing infrastructure involves designing systems optimized for the needs of AI, such as powerful processing capabilities, massive data storage, and real-time processing. With advancements in hardware like graphics processing units and tensor processing units, along with sophisticated data management solutions, businesses can better harness the full potential of AI technologies. This specialized infrastructure enhances the performance and scalability of AI applications while enabling faster innovation and more efficient deployment of AI-driven solutions across sectors. Establishing AI-Specific Cloud Computing Infrastructure explores how AI has evolved as a transformative new technology, capable of delivering large incremental value to a wide range of sectors. It examines recent advances in innovation, specifically how computing power, data storage, and digitized data have led to AI-based applications for business and governance. This book covers topics such as digital technology, sustainable development, and artificial intelligence, and is a useful resource for computer engineers, business owners, academicians, data scientists, and researchers.
Generative Ai Current Trends And Applications
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Author : Khalid Raza
language : en
Publisher: Springer Nature
Release Date : 2024-12-09
Generative Ai Current Trends And Applications written by Khalid Raza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-09 with Computers categories.
This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.