Large Language Models: An Introduction (MLI Generative AI Series)
Author:Oswald Campesato (Author)
Publisher finelybook 出版社:Mercury Learning and Information
Edition 版本:First Edition
Publication Date 出版日期:2024-09-25
Language 语言:English
Print Length 页数:480pages
ISBN-10:1501523295
ISBN-13:9781501523298
Book Description
This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, Meta AI, Claude 3, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher.
FEATURES:
Covers in-depth explanations of foundational and advanced LLM concepts, including BERT, GPT-4, and prompt engineering
Uses practical Python code samples in leveraging LLM functionalities effectively
Discusses future trends, ethical considerations, and the evolving landscape of AI technologies
Includes companion files with code, datasets, and images from the book — available from the publisher for downloading (with proof of purchase)
About the Author
Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty books including Google Gemini for Python, Data Cleaning, and GPT-4 for Developers (all Mercury Learning).
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