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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 |
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| 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) |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (344 pages) |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9784873119106 4873119103 |