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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218
10
章 制限付きボルツマンマシンを用いた推薦システム
このため、制限されていないボルツマンマシンは、
1980
年代から
90
年代にかけて、産業的には大きな
成果を上げることができなかった。
10.1.1
制限付きボルツマンマシン（
RBM
）
2000
年代に、
Hinton
らは、元の制限されていないボルツマンマシンの改良版で、産業的な成功を収
めるようになった。この制限付きボルツマンマシン（RBM）には
1
層 の 入 力 層（ 可視層（
visible layer
）と
も ）と
1
層の隠れ層しかなく、さらに、各ニューロンの接続は、他の層へのみに制限されており、同じ
層の他のニューロンへの接続は許されない。つまり、可視層から可視層への接続や、隠れ層から隠れ
層への接続は存在しない
＊
1
。
Hinton
らは、
1
つの
RBM
の隠れ層の出力をもう
1
つの
RBM
の入力層に与えるように積み重ねられ
ることを示した。このような積層は何度も繰り返すことができ、それによって元のデータから徐々に複
雑な隠れた表現を学習できる。このような多数の
RBM
で構成されるネットワークは
1
つの多層ニュー
ラルネットワークモデルだと考えることができる。このようにして
2006
年に深層学習という領域が生ま
れたのだ。
RBM
は、データの背後にある構造を学習する際に確率的（
stochastic
）な手法をとる。一方、例えば
オートエンコーダなどは決定的（
deterministic
）な手法をとることに注意しよう。
10.2
推薦システム
本章では、
RBM
を用いて推薦システム ...
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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.