April 2024
Intermediate to advanced
336 pages
3h 27m
Japanese
Content preview from ゼロから作るDeep Learning ❺ ―生成モデル編
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ステップ5EMアルゴリズム
このステップのテーマはEMアルゴリズム[6]です。EMアルゴリズムを使用することで、混合ガウスモデルのパラメータ推定が効率的に行えます。ここではEMアルゴリズムを導出し、その実装を行います。
5.1 KLダイバージェンス
EMアルゴリズムの導出には、KLダイバージェンスが重要な役割を果たします。ここでは事前準備として、KLダイバージェンスについて学びます。また、これから先は数式が増えるため、数式の表記方法について2点変更を加えます。まずはその変更点について説明します。
5.1.1 数式の表記について
数式の表記について、1つ目の変更点は「期待値」についてです。ここでは連続型の確率変数
があり、その確率密度が
で表されるものとします。このとき関数
の期待値は次の式で表します。
これまで期待値はのように表記してきましたが、これから先は、上の式のようにと表記します。この表記方法により、に関する期待値であることを明示します。たとえば、確率分布に関する期待値であれば、次の式で表されます。 ...
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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.