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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161
7
章
オートエンコーダ
これまでの
6
章で、教師なし学習を用いて次元削減とクラスタリングを行う方法を見てきた。さらに
それらを用いて、異常検出を行うアプリケーションと類似性に基づいてグループ分けを行うアプリケー
ションを構築した。
しかし教師なし学習にはほかにもできることがある。教師なし学習が得意な分野の
1
つに、特徴量抽
出（
feature extraction
）がある。特徴量抽出とは、元の特徴量集合から新しい特徴量表現を生成するこ
とだ。新しく得た特徴量表現は学習した表現（
learned representation
）と呼ばれ、教師あり学習問題の
性能を改善するために利用される。つまり特徴量抽出によって、教師なし学習を教師あり学習のため
の手段として使うわけだ。
オートエンコーダは特徴量抽出手法の
1
つだ。オートエンコーダでは順伝搬でループのないニューラ
ルネットワークを用いて表現学習（
representation learning
）を行う。表現学習は、ニューラルネットワー
クを含む機械学習の一分野の中核をなす技術だ。
オートエンコーダは表現学習の
1
つだ。ニューラルネットワークの各層は、元の特徴量の表現を学習
する。次の層は、直前の層が学習した表現を用いて学習する。層が進むごとに単純な表現から驚くほ
ど複雑な表現を学習し、概念の階層と呼ばれる構造を構築する。この階層は上に行けば行くほど抽象
的になる。
オートエンコーダの出力が、最終的な新しく元の特徴量から学習した表現になる。この学習した表
現を教師あり学 ...
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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.