Large Language Models

Oswald Campesato

ISBN 10: 1501523295 / ISBN 13: 9781501523298

Editore: Mercury Learning And Information Sep 2024, 2024

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 480 pp. Englisch.

Codice articolo 9781501523298

Dati bibliografici

• Titolo: Large Language Models
• Editore: Mercury Learning And Information Sep 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 1501523295
• ISBN 13: 9781501523298
• Rilegatura: Taschenbuch
• Condizione: Neu
• Peso: 921 grammi

Descrizione articolo

Riassunto

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, 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.

Informazioni sull?autore

Oswald Campesato (San Francisco, CA) 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,
LLMs for Developers, and GPT-4 for Developers (all Mercury Learning).