Neuware -AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 403 pp. Englisch.

Bestandsnummer des Verk�ufers 9781098150969

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Bibliografische Details

Titel: Hands-On Large Language Models
Verlag: O'reilly Media Sep 2024
Erscheinungsjahr: 2024
Sprache: Englisch
ISBN-10: 1098150961
ISBN-13: 9781098150969
Einband: Taschenbuch
Zustand: Neu

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Inhaltsangabe

AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today.

You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings.

This book also helps you:

Understand the architecture of Transformer language models that excel at text generation and representation
Build advanced LLM pipelines to cluster text documents and explore the topics they cover
Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers
Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation
Gain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning

�ber die Autorin bzw. den Autor

Jay Alammar is Director and Engineering Fellow at Cohere (pioneering provider of large language models as an API). In this role, he advises and educates enterprises and the developer community on using language models for practical use cases). Through his popular AI/ML blog, Jay has helped millions of researchers and engineers visually understand machine learning tools and concepts from the basic (ending up in the documentation of packages like NumPy and pandas) to the cutting-edge (Transformers, BERT, GPT-3, Stable Diffusion). Jay is also a co-creator of popular machine learning and natural language processing courses on Deeplearning.ai and Udacity.

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