How to Build Generative AI LLM Models: A Comprehensive Guide to Design, Train, and Deploy Advanced Language Models
2024/11/02
再生時間： 48 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

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

無料で聴く

ポッドキャストの詳細を見る

サマリー
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.

続きを読む一部表示

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 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.

続きを読む一部表示