A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917917057089536 |
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| author | Kulbach, Cedric Cazzonelli, Lucas Ngo, Hoang-Anh Le-Nguyen, Minh-Huong Bifet, Albert |
| author_facet | Kulbach, Cedric Cazzonelli, Lucas Ngo, Hoang-Anh Le-Nguyen, Minh-Huong Bifet, Albert |
| contents | Machine learning algorithms have become indispensable in today's world. They support and accelerate the way we make decisions based on the data at hand. This acceleration means that data structures that were valid at one moment could no longer be valid in the future. With these changing data structures, it is necessary to adapt machine learning (ML) systems incrementally to the new data. This is done with the use of online learning or continuous ML technologies. While deep learning technologies have shown exceptional performance on predefined datasets, they have not been widely applied to online, streaming, and continuous learning. In this retrospective of our tutorial titled Opportunities and Challenges of Online Deep Learning held at ECML PKDD 2023, we provide a brief overview of the opportunities but also the potential pitfalls for the application of neural networks in online learning environments using the frameworks River and Deep-River. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning Kulbach, Cedric Cazzonelli, Lucas Ngo, Hoang-Anh Le-Nguyen, Minh-Huong Bifet, Albert Machine Learning Machine learning algorithms have become indispensable in today's world. They support and accelerate the way we make decisions based on the data at hand. This acceleration means that data structures that were valid at one moment could no longer be valid in the future. With these changing data structures, it is necessary to adapt machine learning (ML) systems incrementally to the new data. This is done with the use of online learning or continuous ML technologies. While deep learning technologies have shown exceptional performance on predefined datasets, they have not been widely applied to online, streaming, and continuous learning. In this retrospective of our tutorial titled Opportunities and Challenges of Online Deep Learning held at ECML PKDD 2023, we provide a brief overview of the opportunities but also the potential pitfalls for the application of neural networks in online learning environments using the frameworks River and Deep-River. |
| title | A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.17222 |