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Ej skönlitteratur, Text · Engelska

Deep learning

Ian Goodfellow (Författare), Yoshua Bengio (Författare), Aaron Courville (Författare)
LCCN 2016022992, ISBN 9780262035613 · (hardcover : alk. paper), ISBN 0262035618 · (hardcover : alk. paper)
utgivning
Cambridge, MA : MIT Press, 2016, [2016]
xxii, 775 pages
Seriemedlemskap
Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Bok

Tillgänglighet utifrån medietyp

Finns online:

Open access (html)
kategori
Ej skönlitteratur
ämne
Machine learning, Maskininlärning
Fler detaljer Referera

Sammanfattning

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Innehållsförteckning

1. Introduction -- PART I. Applied Math and Machine Learning Basics -- 2. Linear Algebra -- 3. Probability and Information Theory -- 4. Numerical Computation -- 5. Machine Learning Basics -- PART II. Deep Networks: Modern Practices -- 6. Deep Feedforward Networks -- 7. Regularization for Deep Learning -- 8. Optimization for Training Deep Models -- 9. Convolutional Networks -- 10. Sequence Modeling: Recurrent and Recursive Nets -- 11. Practical Methodology -- 12. Applications -- PART III. Deep Learning Research -- 13. Linear Factor Models -- 14. Autoencoders -- 15. Representation Learning -- 16. Structured Probabilistic Models for Deep Learning -- 17. Monte Carlo Methods -- 18. Confronting the Partition Function -- 19. Approximate Inference -- 20. Deep Generative Models

Detaljer

Medverkan och funktion
Ian Goodfellow (Författare), Yoshua Bengio (Författare), Aaron Courville (Författare)
klassifikation
006.31 (DDK-klassifikation), 68T07 (Mathematical subject classification)
Identifikator
LCCN 2016022992, ISBN 9780262035613 · (hardcover : alk. paper), ISBN 0262035618 · (hardcover : alk. paper)
har titel
Deep learning
upphovsuppgift
Ian Goodfellow, Yoshua Bengio, and Aaron Courville
utgivning
Cambridge, MA : MIT Press, 2016, [2016]
copyright
2016
omfång
xxii, 775 pages
annan relaterad resurs
Open access (html)
anmärkning
  • Includes bibliographical references and index.
Övriga fysiska detaljer
illustrations
Illustrativt innehåll
Illustrationer finns men typen specificeras ej
klassifikation
Q325.5 (LC-klassifikation)
Seriemedlemskap
kontrollnummer
19973915

Biblioteksspecifik information (bestånd)

  • Chalmers tekniska högskola · Huvudbiblioteket (Z)

    klassifikation
    Q 360 (LC-klassifikation), QA 273 (LC-klassifikation), QA 76.95 (LC-klassifikation)

  • Högskolan i Jönköping, biblioteket (Jon)

    ämne
    Maskininlärning, machine learning

  • Lunds universitets bibliotek · Fysik- och astronomibiblioteket (Lfa)

    ämne
    Computational Physics

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