| 书目名称 | Large Language Models: A Deep Dive | | 副标题 | Bridging Theory and | | 编辑 | Uday Kamath,Kevin Keenan,Sarah Sorenson | | 视频video | | | 概述 | Comprehensive examination of LLMs, from foundational theories to latest advancements, for a thorough understanding.Emphasizes practical applications and industry use cases, guiding readers to solve re | | 图书封面 |  | | 描述 | .Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs—their intricate architecture, underlying algorithms, and ethical considerations—require thorough exploration, creating a need for a comprehensive book on this subject...This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such a | | 出版日期 | Book 2024 | | 关键词 | Large Language Models (LLM); Foundation Models; Generative AI; Prompt Engineering; Instruction Tuning; Re | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-65647-7 | | isbn_ebook | 978-3-031-65647-7 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:51:23
