Summary of A Step-by-step Introduction to the Implementation Of Automatic Differentiation, by Yu-hsueh Fang et al.
A Step-by-step Introduction to the Implementation of Automatic Differentiation
by Yu-Hsueh Fang, He-Zhe Lin, Jie-Jyun Liu, Chih-Jen Lin
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers aim to address the challenge of teaching students about automatic differentiation in deep learning. Despite existing surveys and sophisticated implementations, it is difficult to directly teach students how to implement these systems due to their complexity. To fill this gap, the authors provide a step-by-step introduction to implementing a simple automatic differentiation system. By streamlining mathematical concepts and implementation details, the paper offers a natural setting for understanding how to apply automatic differentiation in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automatic differentiation is an important part of deep learning, but it can be hard to teach students how to use it. Existing surveys explain the basics well, but implementing sophisticated systems is tricky. The authors want to help by giving a simple example of how to implement automatic differentiation from scratch. They’ll make the math and code easy to follow so you can see how it works. |
Keywords
* Artificial intelligence * Deep learning
