• Large Language Models Essentials: Techniques, Tools, and Applications

  • 著者： Anand V
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Large Language Models Essentials: Techniques, Tools, and Applications

著者： Anand V
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  • Comprehensive guide to large language models (LLMs), artificial intelligence systems designed to understand and manipulate human language. It covers the history and evolution of LLMs, including key concepts like the Transformer architecture and attention mechanisms. The document then explores popular LLM models, such as GPT-3 and BERT, along with their use cases and applications in various industries, including business, finance, marketing, entertainment, and healthcare. The text further details the training process for LLMs, including data collection, preprocessing, and optimization technique
    Anand V
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あらすじ・解説

Comprehensive guide to large language models (LLMs), artificial intelligence systems designed to understand and manipulate human language. It covers the history and evolution of LLMs, including key concepts like the Transformer architecture and attention mechanisms. The document then explores popular LLM models, such as GPT-3 and BERT, along with their use cases and applications in various industries, including business, finance, marketing, entertainment, and healthcare. The text further details the training process for LLMs, including data collection, preprocessing, and optimization technique
Anand V
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エピソード
  • Large Language Models Essentials: Techniques, Tools, and Applications

    Large Language Models Essentials: Techniques, Tools, and Applications
    2024/10/18

    Comprehensive guide to large language models (LLMs), artificial intelligence systems designed to understand and manipulate human language. It covers the history and evolution of LLMs, including key concepts like the Transformer architecture and attention mechanisms. The document then explores popular LLM models, such as GPT-3 and BERT, along with their use cases and applications in various industries, including business, finance, marketing, entertainment, and healthcare. The text further details the training process for LLMs, including data collection, preprocessing, and optimization techniques. It also addresses ethical and societal impacts, including bias mitigation, fairness, and responsible AI development. Finally, the guide provides practical implementation steps, including setting up a development environment, building and training a simple LLM, and fine-tuning pretrained models.

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    11 分

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