Summary of Mathematics Of Neural Networks (lecture Notes Graduate Course), by Bart M.n. Smets
Mathematics of Neural Networks (Lecture Notes Graduate Course)
by Bart M.N. Smets
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents the lecture notes from a course on neural networks taught at Eindhoven University of Technology from 2021 to 2023. The course aimed to introduce mathematics students at the graduate level to neural networks and foster their interest in further research. The lecture notes are divided into two parts: an introduction to deep learning, focusing on formal mathematical approaches; and an exploration of Lie group theory and its application to designing neural networks with desirable geometric properties. The notes were designed to be self-contained and accessible for students with a moderate mathematics background. The course also included coding tutorials and assignments in Jupyter notebooks, which are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This is a set of lecture notes from a university course about neural networks. The course was taught at Eindhoven University of Technology and aimed to help graduate-level math students learn about neural networks. The notes cover two main topics: deep learning and how it’s used in neural networks, and the theory behind designing neural networks that have certain properties. The goal is to make the material easy for anyone with a moderate math background to understand. The course also included coding exercises and projects, which are available online. |
Keywords
* Artificial intelligence * Deep learning
