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

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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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 trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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 trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

La IA ha adquirido nuevas y sorprendentes capacidades linguisticas en los ultimos anos. Impulsados por los rapidos avances en Deep Learning, los sistemas de IA linguistica son capaces de escribir y comprender textos mejor que nunca. Esta tendencia esta dando lugar a nuevas funciones, productos e industrias enteras. A traves de la naturaleza visualmente educativa de este libro, los lectores aprenderan herramientas practicas y conceptos que necesitan para utilizar estas capacidades hoy en dia.Aprenderas a utilizar modelos de lenguaje de gran tamano previamente entrenados para casos de uso como la redaccion publicitaria y la sintesis; a crear sistemas de busqueda semantica que van mas alla de la coincidencia de palabras clave; y a utilizar bibliotecas existentes y modelos previamente entrenados para la clasificacion, busqueda y agrupacion de textos. Este libro tambien te ayuda a:Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generacion y representacion de texto.Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generacion y representacion de texto.Construir canalizaciones avanzadas de LLM para agrupar documentos de texto y explorar los temas que tratanConstruir motores de busqueda semantica que vayan mas alla de la busqueda por palabras clave, utilizando metodos como la recuperacion densa y los reordenadores.Explora como se pueden utilizar los modelos generativos, desde la ingenieria de prompts hasta la generacion aumentada por recuperacion.Obten una comprension mas profunda de como entrenar LLMs y optimizarlos para aplicaciones especificas utilizando el ajuste fino de modelos generativos, el ajuste fino contrastivo y el aprendizaje en contexto.

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

Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understandand effectively work withthis emerging technology.Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.Explore the core architecture of AI agents: tools, memory, and planningUnderstand reasoning LLMs, multimodal models, and multi-agent collaborationLearn advanced methods, including distillation, quantization, and reinforcement learningEvaluate real-world applications, strengths, and limitations of AI agents

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

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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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 trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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 trainedUnderstanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

La IA ha adquirido nuevas y sorprendentes capacidades linguisticas en los ultimos anos. Impulsados por los rapidos avances en Deep Learning, los sistemas de IA linguistica son capaces de escribir y comprender textos mejor que nunca. Esta tendencia esta dando lugar a nuevas funciones, productos e industrias enteras. A traves de la naturaleza visualmente educativa de este libro, los lectores aprenderan herramientas practicas y conceptos que necesitan para utilizar estas capacidades hoy en dia.Aprenderas a utilizar modelos de lenguaje de gran tamano previamente entrenados para casos de uso como la redaccion publicitaria y la sintesis; a crear sistemas de busqueda semantica que van mas alla de la coincidencia de palabras clave; y a utilizar bibliotecas existentes y modelos previamente entrenados para la clasificacion, busqueda y agrupacion de textos. Este libro tambien te ayuda a:Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generacion y representacion de texto.Comprender la arquitectura de los modelos de lenguaje Transformer que destacan en la generacion y representacion de texto.Construir canalizaciones avanzadas de LLM para agrupar documentos de texto y explorar los temas que tratanConstruir motores de busqueda semantica que vayan mas alla de la busqueda por palabras clave, utilizando metodos como la recuperacion densa y los reordenadores.Explora como se pueden utilizar los modelos generativos, desde la ingenieria de prompts hasta la generacion aumentada por recuperacion.Obten una comprension mas profunda de como entrenar LLMs y optimizarlos para aplicaciones especificas utilizando el ajuste fino de modelos generativos, el ajuste fino contrastivo y el aprendizaje en contexto.

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

Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understandand effectively work withthis emerging technology.Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.Explore the core architecture of AI agents: tools, memory, and planningUnderstand reasoning LLMs, multimodal models, and multi-agent collaborationLearn advanced methods, including distillation, quantization, and reinforcement learningEvaluate real-world applications, strengths, and limitations of AI agents