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Publicado por O'Reilly Media 3/18/2019, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
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Añadir al carritoPaperback or Softback. Condición: New. Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data 1.2. Book.
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Añadir al carritoCondición: New. 2019. 1st Edition. Paperback. . . . . .
Publicado por O'Reilly Media, Inc, USA, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 645.
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Añadir al carritoCondición: New. 2019. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.
Publicado por Oreilly & Associates Inc, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 76,74
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Añadir al carritoPaperback. Condición: Brand New. 337 pages. 9.00x7.00x0.75 inches. In Stock.
Librería: moluna, Greven, Alemania
EUR 68,34
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Añadir al carritoCondición: New. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-rea.
Publicado por O'reilly Media Apr 2019, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - 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.
Librería: Lakeside Books, Benton Harbor, MI, Estados Unidos de America
EUR 47,35
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Añadir al carritoCondición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Publicado por O'Reilly Media, Sebastopol, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 98,38
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Añadir al carritoPaperback. Condición: new. Paperback. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, 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 shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists 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. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks" Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por O'Reilly Media, Sebastopol, 2019
ISBN 10: 1492035645 ISBN 13: 9781492035640
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 64,40
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Añadir al carritoPaperback. Condición: new. Paperback. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, 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 shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists 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. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks" Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 109,19
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Añadir al carritoPaperback. Condición: New. New. book.