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7変分オートエンコーダ（VAE）
私たちはニューラルネットワークという武器を手にしました。ニューラルネットワークと生成モデルが合わさって、多くのイノベーションが起きています。このステップのテーマは変分オートエンコーダ（Variational AutoEncoder：VAE）[10]です。VAEはニューラルネットワークを活用することで、より複雑な表現が可能になります。VAEの導出は全部で3部構成となり、やや長丁場になります。あせらず、じっくり進みましょう。
7.1 VAEとデコーダ
ここでは図7-1を見ながら、これまで私たちがたどってきた道を振り返ります。
図7-1 （1）正規分布、（2）GMM、（3）VAEの確率分布
まずは図7-1の（1）です。これはデータの確率分布を「1つの正規分布」でモデル化した例です。単純なモデルですが、世の中には1つの正規分布の表現で十分な問題も多くあります。その次が（2）の混合ガウスモデル（GMM）です。GMMは複数の正規分布から構成されるので、1つの正規分布よりも表現力は向上し、より多くの問題に対応できるようになります。しかし、それでもまだ表現できない問題が存在します。
私たちがこれから目指すのは、（3）のような、学習データに合わせて柔軟に形が決まる確率分布です。変分オートエンコーダ（VAE）はニューラルネットワークを活用することで、より複雑な表現が可能となります。図7-1の（3）のような分布も、VAEによって実現できます。本節では、これまで学んだ正規分布とGMMを簡単に振り返り、その流れでVAEへと進みます。 ...
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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.