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

LLM Engineering Series
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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.

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  1. 2024/11/03

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

    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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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.

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