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Super Study Guide: Transformer と大規模言語モデル
大規模言語モデル (LLM) の主要な概念から実践的な応用例まで、簡潔に図解されている学習ガイドです。プロジェクトでの利用、面接対策、個人的な学習にも最適です。
This book is a translation into Japanese of Super Study Guide: Transformers & Large Language Models which was originally written in English
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この本は、面接対策、プロジェクトでの利用、あるいは純粋な好奇心のために、大規模言語モデル (LLM) の仕組みを理解したいすべての方に向けて、簡潔に図解された学習ガイドです。
以下の 5 つの章で構成されています。
- 基礎：ニューラルネットワーク入門、トレーニングと評価に必要な深層学習の概念
- 埋め込み表現：トークン化アルゴリズム、単語埋め込み (word2vec)、文の埋め込み (RNN・LSTM・GRU)
- Transformer：Self-Attention 機構の導入目的、エンコーダ・デコーダ構造の詳細と BERT・GPT・T5 などのモデル、計算を高速化するための工夫
- 大規模言語モデル：プロンプトエンジニアリング、（パラメータ効率の良い）ファインチューニング、プリファレンスチューニングなど、Transformer ベースのモデルを調整するための主な手法
- 応用：感情抽出、機械翻訳、検索拡張生成などを含む一般的な課題
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This book is a translation into Japanese of Super Study Guide: Transformers & Large Language Models which was originally written in English
Author
About the Authors
Afshine Amidi
Afshine Amidi is an Adjunct Professor at Stanford University, where he teaches two AI courses: "CME 295 - Transformers & Large Language Models" and "CME 296 - Diffusion & Large Vision Models". In addition to his academic work, he currently leads LLM efforts for promotional writing at Netflix. Previously, he worked at Uber and at Google on the Gemini team. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from MIT.
Shervine Amidi
Shervine Amidi is an Adjunct Professor at Stanford University where he teaches two AI courses: "CME 295 - Transformers & Large Language Models" and "CME 296 - Diffusion & Large Vision Models". In addition to his academic work, he currently leads LLM efforts for enterprise productivity at Netflix. Previously, he worked at Uber and at Google on the Gemini team. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from Stanford University.
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