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Displaying results 1 to 4 of 4.

  1. Shu li di wen xue
    Author: Li, Mu
    Published: Min guo 79 [1990]
    Publisher:  Li ming wen hua shi ye gong si, Taibei Shi

    Location:
    Bayerische Staatsbibliothek
    Inter-library loan:
    Unlimited inter-library loan, copies and loan
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    Source: Union catalogues
    Language: Chinese
    Media type: Book
    Edition: Chu ban
    Subjects: Chinese literature / 20th century / History and criticism; Alienation (Social psychology) in literature
    Scope: 326 p., 21 cm
  2. San shih nien tai wen yi lun
    Author: Li, Mu
    Published: 1977
    Publisher:  Li Ming, Taipei

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    Source: Union catalogues
    Language: Chinese
    Media type: Book
    Format: Print
    Edition: [2. Aufl.]
    Subjects: Christliche Literatur; Geschichte 1930-1940;
    Scope: 328 S., 8°
    Notes:

    [Chines.] ; [Kritische Betrachtung zur chinesischen Literatur in den 30ger Jahren] ; Aufnahme mit chines, Schriftzeichen im Sinica-Katalog
  3. Sanshi niandai wenyilun
    Author: Li, Mu
    Published: 1977
    Publisher:  Liming wenhua, Taipei

    Location:
    Ruhr-Universität Bochum, Fakultät für Ostasienwissenschaften, Bibliothek
    Signature:
    Bkd 189/1
    Inter-library loan:
    No inter-library loan
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    Source: Union catalogues
    Language: Chinese
    Media type: Book
    Edition: Nachdr.
    Series: Xueshu congshu
    Other subjects: China / Literatur / Geschichte
    Scope: 8, 328 S.
  4. Dive into Deep Learning
    Author: Zhang, Aston; Li, Mu; Lipton, Zachary C.; Smola, Alexander J.
    Published: 2023
    Publisher:  Cambridge University Press, Cambridge

    Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires... more

    Location:
    Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden
    Signature:
    bestellt
    Inter-library loan:
    No inter-library loan

     

    Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required-every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice

     
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    Content information
    Cover (lizenzpflichtig)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9781009389433
    Subjects: COM089000; COM094000; COMPUTERS / Database Management / Data Mining; COMPUTERS / Database Management / General; COMPUTERS / Information Theory; COMPUTERS / Natural Language Processing; Data Mining; Data analysis: general; Data capture & analysis; Data mining; Datenerfassung und -analyse; Datenwissenschaft und -analyse: allgemein; Information theory; Informationstheorie; LANGUAGE ARTS & DISCIPLINES / Library & Information Science; Machine learning; Maschinelles Lernen; Natural language & machine translation; Natürliche Sprachen und maschinelle Übersetzung
    Scope: 574 Seiten
    Notes:

    Installation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.