Large Language Models: An Introduction (MLI Generative AI Series)
by: Oswald Campesato (Author)
Publisher: Mercury Learning and Information
Edition: First Edition
Publication Date: 2024/9/30
Language: English
Print Length: 480 pages
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 foroptimizing 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 engineeringUses practical Python code samples in leveraging LLM functionalities effectivelyDiscusses future trends, ethical considerations, and the evolving landscape of AI technologiesIncludes companion files with code, datasets, and images from the book — available from the publisher fordownloading (with proof of purchase)
About the Author
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 foroptimizing 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 engineeringUses practical Python code samples in leveraging LLM functionalities effectivelyDiscusses future trends, ethical considerations, and the evolving landscape of AI technologiesIncludes companion files with code, datasets, and images from the book — available from the publisher fordownloading (with proof of purchase)
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