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author: Jay Alammar
2024-09-20
O'Reilly Media
Hands-On Large Language Models: Language Understanding And Generation | Alammar, Jay - Grootendorst, Maarten
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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 to
Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
Learn various use cases where these models can provide value
Understand the architecture of underlying Transformer models like BERT and GPT
Get a deeper understanding of how LLMs are trained
Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning
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 to
Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
Learn various use cases where these models can provide value
Understand the architecture of underlying Transformer models like BERT and GPT
Get a deeper understanding of how LLMs are trained
Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning
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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 to
Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
Learn various use cases where these models can provide value
Understand the architecture of underlying Transformer models like BERT and GPT
Get a deeper understanding of how LLMs are trained
Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning
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 to
Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
Learn various use cases where these models can provide value
Understand the architecture of underlying Transformer models like BERT and GPT
Get a deeper understanding of how LLMs are trained
Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning
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O'Reilly MediaSpecifications
Books
Number of Pages
400
Publication Date
2024-09-20
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