Hands-On Large Language Models: Language Understanding and Generation

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 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
  • Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)
1145185960
Hands-On Large Language Models: Language Understanding and Generation

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 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
  • Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)
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Hands-On Large Language Models: Language Understanding and Generation

Hands-On Large Language Models: Language Understanding and Generation

Hands-On Large Language Models: Language Understanding and Generation
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Hands-On Large Language Models: Language Understanding and Generation

Hands-On Large Language Models: Language Understanding and Generation

Overview

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 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
  • Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

Product Details

ISBN-13: 9781098150921
Publisher: O'Reilly Media, Incorporated
Publication date: 09/11/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 428
File size: 21 MB
Note: This product may take a few minutes to download.

About the Author

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.


Maarten Grootendorst is a Senior Clinical Data Scientist at IKNL (Netherlands Comprehensive Cancer Organization). He holds master's degrees in organizational psychology, clinical psychology, and data science which he leverages to communicate complex Machine Learning concepts to a wide audience. With his popular blogs, he has reached millions of readers by explaining the fundamentals of Artificial Intelligence--often from a psychological point of view. He is the author and maintainer of several open-source packages that rely on the strength of Large Language Models, such as BERTopic, PolyFuzz, and KeyBERT. His packages are downloaded millions of times and used by data professionals and organizations worldwide.

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