Summary of Mathematical Theory Of Deep Learning, by Philipp Petersen and Jakob Zech
Mathematical theory of deep learning
by Philipp Petersen, Jakob Zech
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: History and Overview (math.HO)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This introduction to the mathematical analysis of deep learning covers three main pillars: approximation theory, optimization theory, and statistical learning theory. The book provides foundational knowledge for students and researchers in mathematics and related fields, presenting rigorous yet accessible results on fundamental results. It prioritizes simplicity over generality, equipping readers with essential mathematical concepts underpinning deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is a type of artificial intelligence that lets computers learn from data without being explicitly programmed. This book helps people understand the math behind how deep learning works. It covers three important areas: how to get close to the right answer, how to make sure the answer is correct, and how to use statistics to find patterns in data. The book is written for students and researchers who want to learn more about the math behind deep learning. |
Keywords
* Artificial intelligence * Deep learning * Optimization
