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Paperback. Zustand: New. AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning.…

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Paperback. Zustand: New. AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning.…

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Zustand: New. 2024. 1st Edition. paperback. . . . . .

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Paperback. Zustand: New. La IA ha adquirido nuevas y sorprendentes capacidades lingüísticas en los últimos años. Impulsados por los rápidos avances en Deep Learning, los sistemas de IA lingüística son capaces de escribir y comprender textos mejor que nunca. Esta tendencia está dando lugar a nuevas funciones, productos e industrias enteras. A través de la naturaleza visualmente educativa de este libro, los lectores aprenderán herramientas prácticas y conceptos que necesitan para utilizar estas capacidades hoy en díaAprenderás a utilizar modelos de lenguaje de gran tamaño previamente entrenados para casos de uso como la redacción publicitaria y la síntesis; a crear sistemas de búsqueda semántica que van más allá de la coincidencia de palabras clave; y a utilizar bibliotecas existentes y modelos previamente entrenados para la clasificación, búsqueda y agrupación de textos. Este libro también te ayuda:Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generación y representación de texto.Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generación y representación de texto.Construir canalizaciones avanzadas de LLM para agrupar documentos de texto y explorar los temas que tratan.Construir motores de búsqueda semántica que vayan más allá de la búsqueda por palabras clave, utilizando métodos como la recuperación densa y los reordenadores.Explora cómo se pueden utilizar los modelos generativos, desde la ingeniería de prompts hasta la generación aumentada por recuperación.Obtén una comprensión más profunda de cómo entrenar LLMs y optimizarlos para aplicaciones específicas utilizando el ajuste fino de modelos generativos, el ajuste fino contrastivo y el aprendizaje en contexto.…

Paperback. Zustand: New. AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning.…

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Zustand: New. &Uumlber den AutorJay 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 .

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Paperback. Zustand: New. AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings.This book also shows you how to:Build advanced LLM pipelines to cluster text documents and explore the topics they belong toBuild semantic search engines that go beyond keyword search with methods like dense retrieval and rerankersLearn various use cases where these models can provide valueUnderstand the architecture of underlying Transformer models like BERT and GPTGet a deeper understanding of how LLMs are trainedOptimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning.…