Summary of Gpgym: a Remote Service Platform with Gaussian Process Regression For Online Learning, by Xiaobing Dai et al.
GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
by Xiaobing Dai, Zewen Yang
First submitted to arxiv on: 17 Dec 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 Machine learning is widely applied across various domains, but its deployment often requires scripting knowledge in specific programming languages like Python, C++, or MATLAB. This barrier limits professionals outside the field from integrating these algorithms into their workflows. To address this, we propose GPgym, a remote service node based on Gaussian process regression that enables experts to seamlessly incorporate machine learning techniques without writing script code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is used in many fields like industry and research. But it’s hard for people who aren’t experts to use these algorithms because they need to write special codes. We’re trying to make it easier by creating a new service called GPgym. It lets experts from different fields use machine learning without writing code. |
Keywords
» Artificial intelligence » Machine learning » Regression
