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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194
8
章 オートエンコーダハンズオン
図8-8 1層の隠れ層と線形活性化関数を用い、ドロップアウトを適用したスパース過完備オートエンコーダの
評価基準
10
回実行した場合の結果の分布を下に示す。平均適合率の平均は
0.24
で、これまでで最良だったと
きの
0.53
よりも悪い。変動係数は
0.62
だ。
Mean average precision over 10 runs: 0.2426994231628755
Coefifcient of variation over 10 runs: 0.6153219870606188
[0.6078198313533932,
0.20862366991302814,
0.25854513247057875,
0.08496595007072019,
0.26313491674585093,
0.17001322998258625,
0.15338215561753896,
0.1439107390306835,
0.4073422280287587,
0.1292563784156162]
8.11
ノイズのあるデータセットへの対応
実世界のデータによくある問題の
1
つとして、ノイズがある。データを取得する過程やコピーや変化
時の問題でデータの質に問題が生じ、何らかの形で歪んでいることがよくある。このようなノイズに対
して十分頑健で、ノイズに騙されることなくデータの背後にある本当に重要な構造を学習するように、
オートエンコーダを構築する必要がある。
ノイズをシミュレートするため、ガ
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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.