Mathematical Aspects of Deep Learning

Herausgeber: Kutyniok, Gitta; Grohs, Philipp

• Gebundenes Buch

Produktbeschreibung

The development of a theoretical foundation for deep learning methods constitutes one of the most active and exciting research topics in applied mathematics. Written by leading experts in the field, this book acts as a mathematical introduction to deep learning for researchers and graduate students trying to get into the field.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 492
• Erscheinungstermin: 22. Dezember 2022
• Englisch
• Abmessung: 247mm x 171mm x 28mm
• Gewicht: 1068g
• ISBN-13: 9781316516782
• ISBN-10: 1316516784
• Artikelnr.: 63977714

Inhaltsangabe

1. The modern mathematics of deep learning Julius Berner, Philipp Grohs,
Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio; 3. Expressivity
of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok; 4.
Optimization landscape of neural networks René Vidal, Zhihui Zhu and
Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and
recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman,
Grégoire Montavon and Klaus-Robert Müller; 6. Stochastic feedforward neural
networks: universal approximation Thomas Merkh and Guido Montúfar; 7. Deep
learning as sparsity enforcing algorithms A. Aberdam and J. Sulam; 8. The
scattering transform Joan Bruna; 9. Deep generative models and inverse
problems Alexandros G. Dimakis; 10. A dynamical systems and optimal control
approach to deep learning Weinan E, Jiequn Han and Qianxiao Li; 11.
Bridging many-body quantum physics and deep learning via tensor networks
Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua.