July 2026
Intermediate
552 pages
7h 40m
Japanese
Content preview from AIがわかる数学入門 ―グラフ理論、特異値分解、OR：AIシステムを理解するための数学ガイド
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4章ニューラルネットワークの最適化
私の最初のハッとした瞬間は、私たちの脳も世界のモデルを学習していることを知ったときだった。
－ H.
さまざまな人工ニューラルネットワークは、そのアーキテクチャに全結合層を持ちます。本章では、全結合ニューラルネットワークの数学がどのように機能するかを説明し、実際のデータセットを使った例を紹介します。さまざまな学習関数と損失関数を設計し、実験します。また、ニューラルネットワークを学習させる際に使用される最適化と誤差逆伝播のステップは、脳で学習がどのように行われるかに似ていることも説明します。脳は、以前見たことのある概念に直面するとニューロン結合を強化し、以前に学習した概念と矛盾する新しい情報を学習すると結合を弱めることで学習します。機械は数値しか理解できません。数学的には、より強い結合はより大きな数に対応し、より弱い結合はより小さな数に対応します。
最後に、さまざまな正則化のテクニックについて、その利点、欠点、使用例について説明します。
4.1 大脳新皮質と人工ニューラルネットワーク
ニューラルネットワークは、何十億ものニューロンが層状に配置された大脳新皮質をモデルにしています。図4-1は脳の大脳新皮質の3つの垂直断面3枚の画像で、図4-2は全結合型の人工ニューラルネットワークの概略図です。
図4-1 サンティアゴ・ラモン・イ・カハールによる大脳新皮質層に関する3枚の図（『Comparative Study of the Sensory Areas ...
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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.