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Hands-On Large Language Models

Hands-On Large Language Models provides practical tools and concepts for using AI's language capabilities. Learn to utilize pretrained models for tasks like copywriting, summarization, and semantic search, while understanding Transformer architecture.

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Hands-On Large Language Models: Language Understanding and Generation is a comprehensive guide authored by Jay Alammar and Maarten Grootendorst. This book delves into the rapidly advancing field of AI language capabilities, driven by deep learning. Readers will gain practical insights into using pretrained large language models for various applications such as copywriting, summarization, and semantic search.

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The book offers a visually educational approach, making complex concepts accessible. It covers the architecture of Transformer language models, which excel at text generation and representation. Readers will learn to build advanced LLM pipelines for text document clustering and topic exploration. Additionally, the book provides guidance on creating semantic search engines that surpass traditional keyword search, employing methods like dense retrieval and rerankers.

Generative models are explored in depth, from prompt engineering to retrieval-augmented generation. The book also addresses training and optimizing LLMs for specific applications through techniques like generative model fine-tuning, contrastive fine-tuning, and in-context learning.

Published by O'Reilly Media, this first edition book is set to release on October 15, 2024. It spans 425 pages and measures 7 x 0.87 x 9.19 inches. Hands-On Large Language Models is an essential resource for anyone looking to harness the power of AI in language understanding and generation.

  • CategoryMachine Learning Platforms

  • Understand Transformer architecture for text generation

  • Build LLM pipelines for text clustering

  • Create semantic search engines beyond keyword search

  • Explore generative models from prompt engineering to retrieval-augmented generation

  • Train and optimize LLMs using fine-tuning techniques

  • Utilize pretrained models for copywriting and summarization

  • Implement semantic search with dense retrieval methods

  • Gain practical tools for AI language capabilities

  • Copywriting
  • Summarization
  • Semantic Search
  • Text Clustering
  • Generative Models

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