auto-drive
FEDOT
| auto-drive | FEDOT | |
|---|---|---|
| 1 | 4 | |
| 2 | 606 | |
| - | 1.7% | |
| 0.0 | 8.4 | |
| over 2 years ago | about 4 hours ago | |
| PureBasic | Python | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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auto-drive
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I made a Neural network trained to play my car driving game
Here is a link to the repo : github
FEDOT
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Winning a Flood-Forecasting Hackathon with Hydrology and AutoML
Hi to everyone! I am a developer of the FEDOT framework, and our team and I (NSS_lab team) recently won a hackathon EmergencyDataHack (rus). There was a recent post on TowardsDataSciense based on our competition things: Winning a Flood-Forecasting Hackathon with Hydrology and AutoML.
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[P] FEDOT - AutoML framework for composite pipelines
Hi! I want to discuss the academic project FEDOT (https://github.com/nccr-itmo/FEDOT) that is devoted to the evolutionary AutoML for composite pipelines. I am one of the developers of this framework and hope to obtain some feedback on this solution and share our experience.
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The experience of the AutoML application in hackathons
ITMO University's Natural Systems Simulation Lab is integrating hackathons into education and research. One of its first results is an application of the AutoML framework called FEDOT that allows obtaining the result that impressed the expert board and brought the lab’s team a victory at a river flood forecasting hackathon organized by the Ministry of Emergency Situations. The final model is hybrid and combined several data-driven models and equation-based domain-specific models.
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FEDOT framework for evolutionary design of composite pipelines
Hi all! I am a member of the academic AI/ML research team. I want to share the information about the development of the open-source AutoML tool for the design of composite ML pipelines using an evolutionary approach. It called FEDOT and available in the https://github.com/nccr-itmo/FEDOT
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