Mathematical theory of deep learning
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909991115423744 |
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| author | Petersen, Philipp Zech, Jakob |
| author_facet | Petersen, Philipp Zech, Jakob |
| contents | This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory. Serving as a guide for students and researchers in mathematics and related fields, the book aims to equip readers with foundational knowledge on the topic. It prioritizes simplicity over generality, and presents rigorous yet accessible results to help build an understanding of the essential mathematical concepts underpinning deep learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mathematical theory of deep learning Petersen, Philipp Zech, Jakob Machine Learning History and Overview This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory. Serving as a guide for students and researchers in mathematics and related fields, the book aims to equip readers with foundational knowledge on the topic. It prioritizes simplicity over generality, and presents rigorous yet accessible results to help build an understanding of the essential mathematical concepts underpinning deep learning. |
| title | Mathematical theory of deep learning |
| topic | Machine Learning History and Overview |
| url | https://arxiv.org/abs/2407.18384 |