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
Developed an end-to-end Lorenz forecasting workflow for the ML4DE_hackathon repository, focusing on hackathon readiness and reproducibility. Delivered a hierarchical shallow PLRNN model for Lorenz time series, implementing model architecture, training scripts, data handling, and evaluation metrics using Python and PyTorch. Enhanced project documentation and streamlined onboarding by updating artifacts, improving the readme, and aligning documentation with the current workflow. Emphasized reproducible results and rapid experimentation by providing complete training and inference pipelines, as well as plotting utilities. No major bug fixes were required, allowing full attention to feature development and demonstration-ready capabilities for contributors and stakeholders.
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
Repositories Contributed To
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