April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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263
12
章
敵 対 的 生 成 ネ ット ワ ー ク
これまでに
2
つの生成モデル、
RBM
と
DBN
を見てきた。本章では、教師なし学習と生成モデルに
おいて最新の、そして最も期待されている領域である敵対的生成ネットワーク（
GAN
：
Generative
Adversarial Network
）を 見 て い く 。
12.1
GAN
の考え方
GAN
は、
2014
年にモントリオール大学の
Ian Goodfellow
らによって提案された。
GAN
には
2
つの
ニューラルネットワークがある。
1
つは生成ネットワーク（
generator
）で、入力として受け取った実際
のデータサンプルから学習したモデルに基づいて、データを生成する。もう一方は識別ネットワーク
（
discriminator
）で、生成ネットワークが生成したデータと真の分布から得たデータを識別する。
わかりやすく例えると、生成ネットワークは偽造屋で、識別ネットワークは偽造品を識別しようとす
る警察のようなものだ。この
2
つのネットワークはゼロサムゲームを演じている。生成ネットワークは
識別ネットワークを騙して合成データが真の分布から得られたものだと思わせようとする。識別ネット
ワークは合成データを偽物だと指摘しようとする。
GAN
の生成ネットワークは、ラベルがまったくなくても真の分布の背後にある構造を学習すること
ができるので、教師なし学習アルゴリズムであると言える。生成ネットワークは、訓練対象のデータよ
りもはるかに少数のパラメータを用いて、データの背後にある構造を学 ...
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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.