February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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1
出力層
出力層の計算は、次の数式で表すことができます。
Y
=
HW
2
+
B
2
ここで、
H
は第1 層の出力（
n#D
ff
の行列）、
W
2
は重み行列（
D
ff
#D
model
の行列）、
B
2
はバイ
アス項（
n#D
model
の行列）を表しています。
また、FFNN にはドロップアウト層も含まれています。ドロップアウトは、過学習を防ぐ
ための正則化技術です。学習時にニューロンをランダムに無効化することで、モデルが特定の
特徴に過度に依存することを防ぎます。
n
個のトークンからなる入力行列
X
（
n#D
model
の行列）が与えられ、FFNNを通過すること
で、同じ次元の出力行列
Y
（
n#D
model
の行列）が得られます。
1.3.5
出力線形層（Output Linear Layer）
この層は、モデルの最終的な出力を生成します。
n
個の入力トークンそれぞれに対して、次
のトークンの確率分布が出力されます。確率分布は語彙数
|V |
のベクトルですから、この層
の出力は
n#|V |
の行列です。一方、入力は
n
個の
D
model
次元ベクトル、すなわち
n#D
model
の行列です。各行はトークン埋め込みを保持します。
図1.3.16 に、出力線形層の構成を示します。出力線形層は、線形変換層とソフトマックス
関数の二つの部分から構成されています。以降、本項ではこれらを順に説明します。
059
1.3
Transformerの仕組み
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.