How to Build Large Language Models (LLMs): From Data Preparation to Deployment and Beyond

Vemula, Anand

  • 出版商: Independently Published
  • 出版日期: 2024-08-09
  • 售價: $660
  • 貴賓價: 9.5$627
  • 語言: 英文
  • 頁數: 104
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798335439497
  • ISBN-13: 9798335439497
  • 相關分類: LangChain
  • 海外代購書籍(需單獨結帳)

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商品描述

How to Build Large Language Models (LLMs): From Data Preparation to Deployment and Beyond" provides a comprehensive guide to the entire lifecycle of creating and deploying large language models. This book serves as an essential resource for AI practitioners, data scientists, and machine learning engineers interested in mastering the intricacies of LLMs.

The book begins with an introduction to LLMs, covering foundational concepts and the evolution of language models from early recurrent neural networks (RNNs) to modern transformer architectures. It explores popular LLM architectures, including GPT and BERT, highlighting their unique features and applications.

Part II delves into data preparation and management, a crucial phase for building effective LLMs. It provides detailed guidance on sourcing and curating datasets, addressing biases, and ensuring data diversity. Techniques for data preprocessing, such as tokenization and normalization, are discussed along with methods for handling missing data and generating synthetic data. The section also covers data storage and management strategies to design scalable pipelines and ensure data security.

In Part III, the focus shifts to the technical aspects of building the model. It includes setting up the development environment, choosing appropriate model architectures, and deciding between building from scratch or fine-tuning pre-trained models. The book also provides insights into training LLMs, including distributed training techniques and strategies for addressing common challenges like overfitting and underfitting. Hyperparameter tuning and optimization techniques are also covered to enhance model performance.

Part IV addresses evaluating and fine-tuning the model, emphasizing metrics for assessing model performance, fine-tuning techniques, and debugging strategies. It offers practical solutions for improving model accuracy and adapting it to specific use cases.

Finally, Part V explores deployment and maintenance strategies, including deployment options, monitoring, and securing LLMs in production environments. The book concludes with real-world case studies and examples, demonstrating the practical applications of LLMs in various industries

商品描述(中文翻譯)

《如何構建大型語言模型 (LLMs):從數據準備到部署及其後》提供了創建和部署大型語言模型整個生命周期的全面指南。本書是對於希望掌握 LLMs 複雜性的 AI 從業者、數據科學家和機器學習工程師的重要資源。

本書首先介紹 LLMs,涵蓋基礎概念以及語言模型從早期的遞迴神經網絡 (RNNs) 到現代的變壓器架構的演變。它探討了流行的 LLM 架構,包括 GPT 和 BERT,並突顯其獨特特徵和應用。

第二部分深入探討數據準備和管理,這是構建有效 LLMs 的關鍵階段。它提供了有關數據集來源和策劃的詳細指導,解決偏見問題並確保數據多樣性。討論了數據預處理技術,如標記化和正規化,以及處理缺失數據和生成合成數據的方法。本節還涵蓋了數據存儲和管理策略,以設計可擴展的管道並確保數據安全。

在第三部分,重點轉向構建模型的技術方面。包括設置開發環境、選擇合適的模型架構,以及決定是從頭開始構建還是微調預訓練模型。本書還提供了有關訓練 LLMs 的見解,包括分佈式訓練技術和解決常見挑戰(如過擬合和欠擬合)的策略。還涵蓋了超參數調整和優化技術,以提升模型性能。

第四部分探討評估和微調模型,強調評估模型性能的指標、微調技術和除錯策略。它提供了改善模型準確性和將其適應特定用例的實用解決方案。

最後,第五部分探討部署和維護策略,包括部署選項、監控以及在生產環境中保護 LLMs。本書以現實案例研究和示例作結,展示了 LLMs 在各行各業的實際應用。