Mathematical Theory of Deep Learning
Overview & Synopsis
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.
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Document Information
| Document Title | Mathematical Theory of Deep Learning |
|---|---|
| Author / Organization | Philipp Petersen, Jakob Zech |
| Subject & Discipline | Deep Learning, Neural Networks, Linear and Matrix Algebra, Calculus and Mathematical Analysis, Statistics, Probability and Stochastic Processes |
| Categories | Deep Learning |
| Publication Year | 2026 |
| License / Rights | Creative Commons License (CC) |
| Resource Verification | Verification pending |
| Alternative Mirrors & Links | |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH |
| Catalog ID | DOC-268915 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |