m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Data ManagementData ScienceDeep LearningMachine LearningPyTorchPython
JohnJParry
PROFILE
Johnjparry
In April 2025, JJ Parry developed an end-to-end CNN-based prediction engine for the ML4DE_hackathon repository, focusing on data-driven forecasting. Leveraging Python and PyTorch, JJ implemented a complete pipeline encompassing data loading, model definition, training, and prediction generation, with outputs saved as numpy artifacts for downstream analysis. The work included integrating the ks_problem model, updating prediction dimensions for compatibility, and refreshing dependencies to support machine learning workflows and visualization. JJ’s contributions resulted in a reproducible, runnable prototype that streamlines data management and deep learning tasks, demonstrating depth in both technical implementation and workflow integration within the project’s context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
229
Activity Months1
Your Network
7 people
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on delivering an end-to-end CNN-based prediction engine in the ML4DE_hackathon repository, with end-to-end data flow from loading to predictions and artifact generation. Implemented a CNN model, training loop, and prediction pipeline, with results saved as numpy files (e.g., ks_prediction.npy) and updates to dependencies for ML libraries and visualization. Integrated the ks_problem model and updated predictions to ensure reproducibility and compatibility with downstream analysis. The work is backed by a series of commits that introduced the model, fixed prediction dimensions, and refreshed prediction data, enabling a runnable prototype for data-driven forecasting.
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on delivering an end-to-end CNN-based prediction engine in the ML4DE_hackathon repository, with end-to-end data flow from loading to predictions and artifact generation. Implemented a CNN model, training loop, and prediction pipeline, with results saved as numpy files (e.g., ks_prediction.npy) and updates to dependencies for ML libraries and visualization. Integrated the ks_problem model and updated predictions to ensure reproducibility and compatibility with downstream analysis. The work is backed by a series of commits that introduced the model, fixed prediction dimensions, and refreshed prediction data, enabling a runnable prototype for data-driven forecasting.
April 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ManagementData ScienceDeep LearningMachine LearningPyTorchPython
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