m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon
Apr 2025 – Apr 2025
1 Month active
Languages Used
CSSHTMLJinjaJupyter NotebookPython
Technical Skills
Backend DevelopmentData ScienceDeep LearningDeepXDEFlaskJupyter Notebook
GeorgAUT
PROFILE
Georgaut
Developed the foundational project structure for the ML4DE_hackathon repository, focusing on backend and scientific computing workflows. Built a Flask-based application scaffold and implemented data generation and evaluation scripts to support machine learning experiments. Enhanced repository hygiene by refactoring directories, updating baseline configurations, and removing obsolete files, which improved maintainability and onboarding for new contributors. Addressed model robustness by enabling fallback training from scratch when pretrained models were unavailable. Leveraged Python, Jupyter Notebook, and SciPy to streamline reproducibility and accelerate sprint velocity. The work emphasized clarity, reproducibility, and efficient collaboration within a deep learning and scientific computing context.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
2
Lines of code
1,890
Activity Months1
Your Network
74 peopleShared Repositories
7
Work History
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for ML4DE_hackathon: Focused on delivering a production-ready project skeleton, aligning evaluation baselines, and streamlining repository hygiene to boost onboarding, reproducibility, and sprint velocity. Delivered a Flask-based app scaffold, data generation/evaluation scripts, baseline data updates, resilience in model loading (train-from-scratch fallback), and removal of obsolete folders to reduce clutter and maintenance risk.
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for ML4DE_hackathon: Focused on delivering a production-ready project skeleton, aligning evaluation baselines, and streamlining repository hygiene to boost onboarding, reproducibility, and sprint velocity. Delivered a Flask-based app scaffold, data generation/evaluation scripts, baseline data updates, resilience in model loading (train-from-scratch fallback), and removal of obsolete folders to reduce clutter and maintenance risk.
April 2025
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSHTMLJinjaJupyter NotebookPython
Technical Skills
Backend DevelopmentData ScienceDeep LearningDeepXDEFlaskJupyter NotebookMachine LearningMatplotlibNumPyODE SolvingPINNPythonSciPyScientific ComputingWeb Development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline