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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11.6
教師なし学習を用いた教師あり学習手法の改善
253
図 11-10 DBNの再構成誤差
図 11-11に、
DBN
の最後の層、つまり
4
層目の
RBM
の隠れ層が学習した特徴量を示す。
図 11-11 DBNの4 層目のRBMが学習した特徴量
再構成誤差も学習された特徴量も想定通りで、いずれも
RBM
の場合によく似ている。
11.6
教師なし学習を用いた教師あり学習手法の改善
これまで
RBM
と
DBN
で行ってきたのは教師なし学習だけで、画像のラベルはまったく使ってこな
かった。元の
MNIST
画像の
50,000
のデータから潜在特徴量を学習することで、生成モデルを構築し
てきた。この生成モデルは再構成誤差を最小化するように学習しているので、元の画像とよく似た画
像を生成することができる。
ここで一歩立ち戻って、このような生成モデルの有用性を理解してみよう。
世界にあるほとんどのデータはラベル付けされていない。したがって、教師あり学習がいくら強力で
有効であっても、教師なし学習を使ってラベル付けされていないデータを活用しなければならない。教
師あり学習だけでは十分ではないのだ。
教師なし学習の有用性を示すために、
MNIST
の
50,000
枚の画像のうち、一部にだけ、例えば
5,000
枚だけにラベル付けされている状況を考えてみよう。
5,000
枚のラベル付き画像だけでは、教師あり学
254
11
章 深層信念ネットワークを用いた特徴量検出
習を用いた画像クラス分類器は
50,000
枚のラベル付き画像がある場合のようには機能しない。 ...
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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.
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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.