Name: Mathematical Aspects of Deep Learning By Philipp Grohs (Universitat Wien, Austria)
SKU: 9781316516782
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Mathematical Aspects of Deep Learning Summary

Mathematical Aspects of Deep Learning by Philipp Grohs (Universitat Wien, Austria)

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

About Philipp Grohs (Universitat Wien, Austria)

Philipp Grohs is Professor of Applied Mathematics at the University of Vienna and Group Leader of Mathematical Data Science at the Austrian Academy of Sciences. Gitta Kutyniok is Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at Ludwig-Maximilians Universitat Munchen and Adjunct Professor for Machine Learning at the University of Tromso.

1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning K. Kawaguchi, Y. Bengio and L. Kaelbling; 3. Expressivity of deep neural networks Ingo Guhring, Mones Raslan and Gitta Kutyniok; 4. Optimization landscape of neural networks Rene Vidal, Zhihui Zhu and Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Gregoire Montavon and Klaus-Robert Muller; 6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montufar; 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.

Additional information

Sku

NGR9781316516782

ISBN 13

9781316516782

ISBN 10

1316516784

Title

Mathematical Aspects of Deep Learning by Philipp Grohs (Universitat Wien, Austria)

Author

Philipp Grohs (Universitat Wien, Austria)

Condition

New

Binding type

Hardback

Publisher

Cambridge University Press

Year published

2022-12-22

Number of pages

492

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Mathematical Aspects of Deep Learning Philipp Grohs (Universitat Wien, Austria)

Mathematical Aspects of Deep Learning by Philipp Grohs (Universitat Wien, Austria)

Summary

Mathematical Aspects of Deep Learning Summary

Mathematical Aspects of Deep Learning by Philipp Grohs (Universitat Wien, Austria)

About Philipp Grohs (Universitat Wien, Austria)

Table of Contents

Additional information

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