Large Language Models : An Introduction
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, strength...
Enregistré dans:
| Auteur principal: | |
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| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Boston, MA :
Mercury Learning and Information
2024.
Paris : Cyberlibris |
| Accès en ligne: | Accès Université d'Orléans et IFPM |
| Note: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781501520587.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Large Language Models, An Introduction, Oswald Campesato, Boston, MA, Mercury Learning and Information, 2024, 1 vol. (503 p.), 978-15-0152-058-7 |
| Résumé: | 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 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 fordownloading (with proof of purchase) |
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| Description: | Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781501520587.jpg Cyberlibris (ScholarVox) corpus Informatique |
| ISBN: | 9781501520587 |
| Accès: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris |