• LLM in Python: Comprehensive Guide to Building and Deploying Large Language Models

  • 著者： Anand V
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LLM in Python: Comprehensive Guide to Building and Deploying Large Language Models

著者： Anand V
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  • Explaining LLMs, their evolution, and applications in different industries. The book then dives into data preparation and management, including techniques for collecting, cleaning, and storing large datasets. It then guides the reader through building the model, focusing on model architecture design, training techniques, and hyperparameter tuning. After that, the book examines model evaluation and fine-tuning techniques, including common issues and debugging strategies.
    Anand V
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あらすじ・解説

Explaining LLMs, their evolution, and applications in different industries. The book then dives into data preparation and management, including techniques for collecting, cleaning, and storing large datasets. It then guides the reader through building the model, focusing on model architecture design, training techniques, and hyperparameter tuning. After that, the book examines model evaluation and fine-tuning techniques, including common issues and debugging strategies.
Anand V
続きを読む 一部表示
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  • LLM in Python: Comprehensive Guide to Building and Deploying Large Language Models

    LLM in Python: Comprehensive Guide to Building and Deploying Large Language Models
    2024/11/03

    Explaining LLMs, their evolution, and applications in different industries. The book then dives into data preparation and management, including techniques for collecting, cleaning, and storing large datasets. It then guides the reader through building the model, focusing on model architecture design, training techniques, and hyperparameter tuning. After that, the book examines model evaluation and fine-tuning techniques, including common issues and debugging strategies. Finally, it covers deploying and scaling LLMs in production environments, including strategies for handling high traffic and large-scale deployments, and discusses ethical considerations and best practices for using LLMs responsibly.

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    24 分

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