• How to Build Generative AI LLM Models: A Comprehensive Guide to Design, Train, and Deploy Advanced L

  • 著者： Anand V
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How to Build Generative AI LLM Models: A Comprehensive Guide to Design, Train, and Deploy Advanced L

著者： Anand V
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  • An introduction to generative AI and LLMs, outlining their history, applications, and key concepts like tokens, embeddings, and attention mechanisms. The guide then delves into the mathematical and statistical foundations of LLMs, covering essential topics such as probability theory, linear algebra, calculus, and deep learning basics. The main focus is on practical aspects of designing and training LLMs, including data collection, data preprocessing, model architectures, training techniques, evaluation metrics, and fine-tuning. The text further explores deploying LLMs in production environment
    Anand V
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あらすじ・解説

An introduction to generative AI and LLMs, outlining their history, applications, and key concepts like tokens, embeddings, and attention mechanisms. The guide then delves into the mathematical and statistical foundations of LLMs, covering essential topics such as probability theory, linear algebra, calculus, and deep learning basics. The main focus is on practical aspects of designing and training LLMs, including data collection, data preprocessing, model architectures, training techniques, evaluation metrics, and fine-tuning. The text further explores deploying LLMs in production environment
Anand V
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エピソード
  • How to Build Generative AI LLM Models: A Comprehensive Guide to Design, Train, and Deploy Advanced Language Models

    How to Build Generative AI LLM Models: A Comprehensive Guide to Design, Train, and Deploy Advanced Language Models
    2024/11/02

    An introduction to generative AI and LLMs, outlining their history, applications, and key concepts like tokens, embeddings, and attention mechanisms. The guide then delves into the mathematical and statistical foundations of LLMs, covering essential topics such as probability theory, linear algebra, calculus, and deep learning basics. The main focus is on practical aspects of designing and training LLMs, including data collection, data preprocessing, model architectures, training techniques, evaluation metrics, and fine-tuning. The text further explores deploying LLMs in production environments, emphasizing model serving, API development, scalability, monitoring, and maintenance. Finally, it discusses ethical considerations like bias mitigation and regulatory compliance, along with advanced techniques like zero-shot learning, continual learning, and future directions for the field.

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    48 分

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