Praxisbuch Large Language Models

Name: Praxisbuch Large Language Models
Author: Jay Alammar|Maarten Grootendorst
ISBN: 9783960092667

Jay Alammar|Maarten Grootendorst

Language: German

Published by dpunkt.verlag GmbH, 2025

3960092660 / 9783960092667

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Autor/Autorin: Jay AlammarJay Alammar ist Direktor und Engineering Fellow bei Cohere, dem Vorreiter bei der Bereitstellung von Large Language Models als API. In dieser Funktion beraet und schult er Unternehmen und die Entwicklergemeinschaft bei der .

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Title: Praxisbuch Large Language Models
Author: Jay Alammar|Maarten Grootendorst
Publisher: dpunkt.verlag GmbH
Publication year: 2025
Condition: New
Binding: Soft cover
Language: German
ISBN 10: 3960092660
ISBN 13: 9783960092667
Dimensions: 2.4 x 24 x 16.7

Alle Werkzeuge und Techniken für die praktische Arbeit mit LLMs - Das Handbuch für das intuitive Verständnis von LLMs: Mit zahlreichen Visualisierungen, die Konzepte schnell zugänglich machen - Themen sind die Sprachverarbeitung - Textklassifikation, Suche oder Cluster - und die Sprachgenerierung - vom Prompt Engineering bis zur Retrieval Augmented Generation (RAG) - Die Autoren haben mit ihren beliebten Blogs Millionen von Entwickler*innen geholfen, Machine Learning und KI zu verstehen Diese umfassende und anschauliche Einführung in die Welt der LLMs beschreibt sowohl konzeptionelle Grundlagen als auch konkrete Anwendungen und nützliche Tools. Tauchen Sie in das Innenleben von LLMs ein und erkunden Sie ihre Architekturen, Einsatzbereiche, Trainingsmethoden und Feintuning-Techniken. Mit seiner einzigartigen Mischung aus intuitiv verständlichen Illustrationen und praxisbezogenen Erläuterungen ist dieses Buch die ideale Ausgangsbasis für alle, die die Möglichkeiten von KI-Systemen voll ausschöpfen möchten. Sie lernen, vortrainierte Transformer-LLMs von Hugging Face für Anwendungsfälle wie das Verfassen von Texten oder für Inhaltszusammenfassungen einzusetzen. Sie erfahren außerdem, wie Sie Suchsysteme erstellen und vorhandene Bibliotheken und vortrainierte Modelle für Textklassifikation, Suche und Clustering nutzen. - Verstehen Sie die Architektur von Transformer-basierten Sprachmodellen, die bei der Textgenerierung und -repräsentation hervorragende Ergebnisse liefern - Entwerfen Sie fortgeschrittene LLM-Pipelines, um Textdokumente zu clustern und die darin enthaltenen Themen zu erforschen - Erstellen Sie semantische Suchmaschinen, die über den Abgleich von Schlagwörtern hinausgehen und auf Methoden wie Dense Retrieval und Reranking basieren - Lernen Sie, wie Sie generative Modelle optimal einsetzen - vom Prompt Engineering bis hin zur Retrieval Augmented Generation (RAG) - Entwickeln Sie ein tieferes Verständnis dafür, wie LLMs trainiert und für spezifische Anwendungen optimiert werden, beispielsweise durch Feintuning generativer Modelle, Contrastive Fine-Tuning und In-Context-Learning

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