[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_30":-5,"_50":-5,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60,"_61":62,"_63":64,"_65":66,"_67":68,"_69":70},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjMzOTE","title","Super Study Guide: Transformer と大規模言語モデル","state","published","contactAuthorMethod","email","aboutText","

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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