Introduction to Foundation Models by Pin-Yu Chen, Sijia Liu

Download Book ▶️ Link

Read Book Online ▶️ Link

Introduction to Foundation Models
Pin-Yu Chen, Sijia Liu
Page: 310
Format: pdf, ePub, mobi, fb2, azw3, kfx
ISBN: 9783031767692
Publisher: Springer Nature Switzerland

Download or Read Online Introduction to Foundation Models Free Book (PDF ePub Mobi) by Pin-Yu Chen, Sijia Liu Introduction to Foundation Models Pin-Yu Chen, Sijia Liu PDF, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Epub Windows, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Read Online, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Audiobook, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu VK, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Kindle, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Epub MacOS, Introduction to Foundation Models Pin-Yu Chen, Sijia Liu Free Download

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.

[PDF] Foundation Models
       Key questions for foundation models are. – How to train them (what architecture, what data, what objective). – How to apply them, e.g.
Foundation Models and LLMs: a Complete Guide - Kili Technology
       Book a Demo · Home; /; Foundation Models . Foundation Models and LLMs: a . The introduction of Alpaca demonstrated then how these models can still .
GitHub - MathFoundationRL/Book-Mathematical-Foundation-of .
       This book aims to provide a mathematical but friendly introduction to the fundamental concepts, basic problems, and classic algorithms in reinforcement .
Introduction to Foundation Models - Bookswagon
       This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly .
Foundation Models for Natural Language Processing: Pre-trained .
       This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models
Introduction to foundation models on Google Cloud (GCP)
       Google's PaLM model is an example of a powerful text-based foundation model available in Vertex AI. Image Models: These models are used for .
Single-cell Bio Foundation Models: A beginner's overview - Helical AI
       If the Human Genome Project provided us with the book of life, single-cell analyses show us how each cell reads this book. These analyses shed light on the .
AI Engineering: Building Applications with Foundation Models
       foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and .