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Python de hajimeru kyōshi nashi gakushū : kikai gakushū no kanōsei o hirogeru raberu nashi dēta no riyō /
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 /
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning can...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Japonés Inglés |
| Publicado: |
Tōkyō-to Shinjuku-ku :
Orairī Japan,
2020.
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| Edición: | Shohan. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | OR_on1311488908 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||unuuu | ||
| 008 | 220420s2020 ja ob 001 0 jpn d | ||
| 040 | |a ORMDA |b eng |e rda |e pn |c ORMDA |d ORMDA |d OCLCO |d OCLCF |d OCLCQ | ||
| 066 | |c $1 | ||
| 020 | |a 9784873119106 |q (electronic bk.) | ||
| 020 | |a 4873119103 |q (electronic bk.) | ||
| 029 | 1 | |a AU@ |b 000071968828 | |
| 035 | |a (OCoLC)1311488908 | ||
| 037 | |a 9784873119106 |b O'Reilly Media | ||
| 041 | 1 | |a jpn |h eng | |
| 050 | 4 | |a Q325.5 | |
| 082 | 0 | 4 | |a 006.31 |2 23/eng/20220420 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Patel, Ankur A., |e author. | |
| 240 | 1 | 0 | |a Hands-on unsupervised learning using Python. |l Japanese |
| 245 | 1 | 0 | |6 880-01 |a Python de hajimeru kyōshi nashi gakushū : |b kikai gakushū no kanōsei o hirogeru raberu nashi dēta no riyō / |c Ankur A. Patel cho ; Nakada Hidemoto yaku. |
| 250 | |6 880-02 |a Shohan. | ||
| 264 | 1 | |6 880-03 |a Tōkyō-to Shinjuku-ku : |b Orairī Japan, |c 2020. | |
| 300 | |a 1 online resource (344 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title details screen (O'Reilly, viewed April 20, 2022). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 700 | 1 | |6 880-04 |a Nakada, Hidemoto, |e translator. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9784873119106/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | 1 | 0 | |6 245-01/$1 |a Pythonではじめる教師なし学習 : |b 機械学習の可能性を広げるラベルなしデータの利用 / |c Ankur A.Patel著 ; 中田秀基訳. |
| 880 | |6 250-02/$1 |a 初版. | ||
| 880 | 1 | |6 264-03/$1 |a 東京都新宿区 : |b オライリー・ジャパン, |c 2020. | |
| 880 | |6 520-00/$1 |a "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。" -- |c Provided by publisher. | ||
| 880 | 1 | |6 700-04/$1 |a 中田秀基, |e translator. | |
| 994 | |a 92 |b IZTAP | ||