Introduction To Foundation Models
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Introduction To Foundation Models
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Author : Pin-Yu Chen
language : en
Publisher: Springer Nature
Release Date : 2025-06-12
Introduction To Foundation Models written by Pin-Yu Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Computers categories.
This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.
Introduction To Foundation Models
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Author : Pin-Yu Chen
language : en
Publisher: Springer
Release Date : 2025-06-25
Introduction To Foundation Models written by Pin-Yu Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-25 with Computers categories.
This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.
Introduction To Large Language Models For Business Leaders
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Author : I. Almeida
language : en
Publisher: Now Next Later AI
Release Date : 2023-09-02
Introduction To Large Language Models For Business Leaders written by I. Almeida and has been published by Now Next Later AI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.
Responsible AI Strategy Beyond Fear and Hype - 2025 Edition Finalist for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction. In this comprehensive guide, business leaders will gain a nuanced understanding of large language models (LLMs) and generative AI. The book covers the rapid progress of LLMs, explains technical concepts in non-technical terms, provides business use cases, offers implementation strategies, explores impacts on the workforce, and discusses ethical considerations. Key topics include: - The Evolution of LLMs: From early statistical models to transformer architectures and foundation models. - How LLMS Understand Language: Demystifying key components like self-attention, embeddings, and deep linguistic modeling. - The Art of Inference: Exploring inference parameters for controlling and optimizing LLM outputs. - Appropriate Use Cases: A nuanced look at LLM strengths and limitations across applications like creative writing, conversational agents, search, and coding assistance. - Productivity Gains: Synthesizing the latest research on generative AI's impact on worker efficiency and satisfaction. - The Perils of Automation: Examining risks like automation blindness, deskilling, disrupted teamwork and more if LLMs are deployed without deliberate precautions. - The LLM Value Chain: Analyzing key components, players, trends and strategic considerations. - Computational Power: A deep dive into the staggering compute requirements behind state-of-the-art generative AI. - Open Source vs Big Tech: Exploring the high-stakes battle between open and proprietary approaches to AI development. - The Generative AI Project Lifecycle: A blueprint spanning use case definition, model selection, adaptation, integration and deployment. - Ethical Data Sourcing: Why the training data supply chain proves as crucial as model architecture for responsible development. - Evaluating LLMs: Surveying common benchmarks, their limitations, and holistic alternatives. - Efficient Fine-Tuning: Examining techniques like LoRA and PEFT that adapt LLMs for applications with minimal compute. - Human Feedback: How reinforcement learning incorporating human ratings and demonstrations steers models towards helpfulness. - Ensemble Models and Mixture-of-Experts: Parallels between collaborative intelligence in human teams and AI systems. - Areas of Research and Innovation: Retrieval augmentation, program-aided language models, action-based reasoning and more. - Ethical Deployment: Pragmatic steps for testing, monitoring, seeking feedback, auditing incentives and mitigating risks responsibly. The book offers an impartial narrative aimed at informing readers for thoughtful adoption, maximizing real-world benefits while proactively addressing risks. With this guide, leaders gain integrated perspectives essential to setting sound strategies amidst generative AI's rapid evolution. More Than a Book By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. No credit card required. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically.
An Introduction To Seismic Analysis And Modeling Of Hydraulic Structures
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Author : J. Paul Guyer, P.E., R.A.
language : en
Publisher: Guyer Partners
Release Date : 2018-10-31
An Introduction To Seismic Analysis And Modeling Of Hydraulic Structures written by J. Paul Guyer, P.E., R.A. and has been published by Guyer Partners this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Technology & Engineering categories.
Introductory technical guidance for civil and geotechnical engineers interested seismic engineering of hydraulic structures such as those associated with locks and dams. Here is what is discussed: 1. PROGRESSIVE ANALYSIS METHODOLOGY 2. METHODS OF ANALYSIS 3. MODELING OF STRUCTURAL SYSTEMS 4. EFFECTIVE STIFFNESS 5. DAMPING 6. INTERACTION WITH BACKFILL SOIL 7. PERMANENT SLIDING DISPLACEMENT.
Generative Ai Techniques Models And Applications
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Author : Rajan Gupta
language : en
Publisher: Springer Nature
Release Date : 2025-03-26
Generative Ai Techniques Models And Applications written by Rajan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Computers categories.
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from: Clear explanations of advanced concepts. Practical implementation insights. Current industry applications. Ethical framework discussions. Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
Building Llm Powered Applications
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Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-22
Building Llm Powered Applications written by Valentina Alto 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 2024-05-22 with Computers categories.
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.
Genai On Aws
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Author : Olivier Bergeret
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-19
Genai On Aws written by Olivier Bergeret 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 2025-03-19 with Computers categories.
The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.
A Practical Guide To Generative Ai Using Amazon Bedrock
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Author : Avik Bhattacharjee
language : en
Publisher: Springer Nature
Release Date : 2025-07-08
A Practical Guide To Generative Ai Using Amazon Bedrock written by Avik Bhattacharjee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Computers categories.
This comprehensive guide gives you the knowledge and skills you need to excel in Generative AI. From understanding the fundamentals to mastering techniques, this book offers a step-by-step approach to leverage Amazon Bedrock to build, deploy, and secure Generative AI applications. The book presents structured chapters and practical examples to delve into key concepts such as prompt engineering, retrieval-augmented generation, and model evaluation. You will gain profound insights into the Amazon Bedrock platform. The book covers setup, life cycle management, and integration with Amazon SageMaker. The book emphasizes real-world applications, and provides use cases and best practices across industries on topics such as text summarization, image generation, and conversational AI bots. The book tackles vital topics including data privacy, security, responsible AI practices, and guidance on navigating governance and monitoring challenges while ensuring adherence to ethical standards and regulations. The book provides the tools and knowledge needed to excel in the rapidly evolving field of Generative AI. Whether you're a data scientist, AI engineer, or business professional, this book will empower you to harness the full potential of Generative AI and drive innovation in your organization. What You Will Learn Understand the fundamentals of Generative AI and Amazon Bedrock Build Responsible Generative AI applications leveraging Amazon Bedrock Know techniques and best practices See real-world applications Integrate and manage platforms Handle securty and governance issues Evaluate and optimze models Gain future-ready insights Understand the project life cycle when building Generative AI Applications Who This Book Is For Data scientistys, AI/ML engineers and architects, software developers plus AI enthusiasts and studenta and educators, and leaders who want to evangelize within organizatios
Pretrain Vision And Large Language Models In Python
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Author : Emily Webber
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31
Pretrain Vision And Large Language Models In Python written by Emily Webber 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 2023-05-31 with Computers categories.
Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples Key Features Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines Explore large-scale distributed training for models and datasets with AWS and SageMaker examples Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring Book Description Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization. With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you'll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models. You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines. By the end of this book, you'll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future. What you will learn Find the right use cases and datasets for pretraining and fine-tuning Prepare for large-scale training with custom accelerators and GPUs Configure environments on AWS and SageMaker to maximize performance Select hyperparameters based on your model and constraints Distribute your model and dataset using many types of parallelism Avoid pitfalls with job restarts, intermittent health checks, and more Evaluate your model with quantitative and qualitative insights Deploy your models with runtime improvements and monitoring pipelines Who this book is for If you're a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.
Artificial Intelligence Foundations Applications And Future Directions
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Author : Ahmet Gürkan YÜKSEK•
language : en
Publisher: Livre de Lyon
Release Date : 2025-03-23
Artificial Intelligence Foundations Applications And Future Directions written by Ahmet Gürkan YÜKSEK• and has been published by Livre de Lyon this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-23 with Computers categories.