m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon
Apr 2025 – Apr 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Deep LearningDocumentationMachine LearningPyTorchPythonRecurrent Neural Networks
gb21553
PROFILE
Gb21553
During April 2025, Rodrigue Nzoyem Ngueguin developed an end-to-end Lorenz forecasting workflow for the ML4DE_hackathon repository, focusing on hackathon readiness and reproducibility. He implemented a hierarchical shallow PLRNN model for Lorenz time series prediction, providing training scripts, data handling, evaluation metrics, and plotting utilities. Using Python, PyTorch, and deep learning techniques, Rodrigue delivered a complete pipeline from initial data preparation to final model artifacts, with thorough documentation to support onboarding and experimentation. His work emphasized robust artifact generation and workflow alignment, resulting in a demonstration-ready system that streamlines contributor engagement and supports rapid model development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
2,899
Activity Months1
Your Network
23 peopleShared Repositories
7
Work History
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for ML4DE_hackathon repo. Focused on delivering an end-to-end Lorenz forecasting workflow and enabling hackathon readiness through robust artifacts, model implementation, and documentation improvements. No major user-facing bug fixes were required this month; the emphasis was on feature completion, reproducibility, and demonstration-ready capabilities.
7 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for ML4DE_hackathon repo. Focused on delivering an end-to-end Lorenz forecasting workflow and enabling hackathon readiness through robust artifacts, model implementation, and documentation improvements. No major user-facing bug fixes were required this month; the emphasis was on feature completion, reproducibility, and demonstration-ready capabilities.
April 2025
Activity
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Quality Metrics
Correctness87.0%
Maintainability87.0%
Architecture87.0%
Performance85.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Deep LearningDocumentationMachine LearningPyTorchPythonRecurrent Neural NetworksTime Series Analysis
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