Large Language Models Essentials: Techniques, Tools, and Applications
2024/10/18
再生時間： 11 分
ポッドキャスト

カートのアイテムが多すぎます

ご購入は五十タイトルがカートに入っている場合のみです。

カートに追加できませんでした。

しばらく経ってから再度お試しください。

ウィッシュリストに追加できませんでした。

しばらく経ってから再度お試しください。

ほしい物リストの削除に失敗しました。

しばらく経ってから再度お試しください。

ポッドキャストのフォローに失敗しました

ポッドキャストのフォロー解除に失敗しました

Large Language Models Essentials: Techniques, Tools, and Applications

無料で聴く

ポッドキャストの詳細を見る

サマリー
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.

続きを読む一部表示

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 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.

続きを読む一部表示