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Название: Large Language Models: An Introduction
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 542
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB
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. This book is intended primarily for people who have a basic knowledge of Generative AI or software developers who are interested in working with LLMs. Specifically, this book is for readers who are accustomed to searching online for more detailed information about technical topics.
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 542
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB
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. This book is intended primarily for people who have a basic knowledge of Generative AI or software developers who are interested in working with LLMs. Specifically, this book is for readers who are accustomed to searching online for more detailed information about technical topics.