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  • 1
    Orig.schr. Ausgabe: 初版.
    Title: PyTorchとfastaiではじめるディープラーニング : : エンジニアのためのAIアプリケーション開発 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119427 , 4873119421
    Language: Japanese
    Pages: 1 online resource (584 pages)
    Edition: Shohan.
    Uniform Title: Deep learning for coders with fastai and PyTorch
    DDC: 006.312
    Keywords: Deep learning (Machine learning) ; Data mining ; Natural language processing (Computer science) ; Python (Computer program language) ; Electronic books
    Abstract: "Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions" --
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  • 2
    Orig.schr. Ausgabe: 初版.
    Title: 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 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119106 , 4873119103
    Language: Japanese
    Pages: 1 online resource (344 pages)
    Edition: Shohan.
    Uniform Title: Hands-on unsupervised learning using Python
    DDC: 006.31
    Keywords: Machine learning ; Artificial intelligence ; Python (Computer program language) ; Artificial Intelligence ; Apprentissage automatique ; Intelligence artificielle ; Python (Langage de programmation) ; artificial intelligence ; Artificial intelligence ; Machine learning ; Python (Computer program language) ; Electronic books
    Abstract: 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.
    Note: Includes bibiographical references and index. - Online resource; title from title details screen (O'Reilly, viewed April 20, 2022)
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