April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Overview
教師なし学習はラベル付けされていないデータから学習する機械学習の一種です。現在の機械学習では大量のラベル付きのデータを用いる教師あり学習が主流ですが、ラベルを付けるには膨大なコストがかかります。現実世界に機械学習を適用していくためには、ラベル付けを必要としない教師なし学習の重要性が増してくると考えられます。本書は実践的な視点から、データにある隠れたパターンを特定し、異常検出や特徴量抽出・選択を行う方法を紹介します。ラベルなしデータを有効に利用することで、機械学習の可能性を各段に広げる教師なし学習の本質に迫ります。
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