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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217
10
章
制限付きボルツマンマシンを用いた
推薦システム
これまでに、教師なし学習を用いてラベルのないデータから背後にある（隠れた）構造を学習した。
具体的には、次元削減を行って高次元のデータセットをはるかに低次元になるように削減して異常検
出するシステムを構築した。また、相互の類似性に基づいてグループ分けするクラスタリングを行った。
本章では、生成教師なしモデル（
generative unsupervised model
）に話を進める。これは元のデータ
セットの確率分布を学習し、それを用いて見たことのないデータに対して推測を行う。次章以降では、
このようなモデル用いて、元のデータと事実上見分けが付かないほど、本物のように見えるデータを生
成する。
これまで本書で見てきたのは、データから観測点を分類する方法を学習する識別モデル
（
discriminative model
）だった。識別モデルはデータから確率分布を学習しない。識別モデルには
「2 章 機械学習プロジェクトのはじめから終わりまで」で学んだロジスティック回帰や決定木も含まれ
るし、「5 章 クラスタリング」で学んだ
k
平均法や階層クラスタリングも含まれる。
まずは、最も簡単な生成教師なしモデルである制限付きボルツマンマシン（
RBM
）から見ていこう。
10.1
ボルツマンマシン
ボルツマンマシン（
Boltzmann machine
）は 、
1985
年に
Geoffrey Hinton
（当時カーネギーメロン大
学の教授。深層学習の父の
1
人とも呼ばれ、現在はト ...
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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.