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
During 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, PyTorch, and deep learning techniques, JJ implemented a complete pipeline encompassing data loading, model definition, training, and prediction generation. The workflow included saving results as numpy artifacts, such as ks_prediction.npy, and updating dependencies to support machine learning and visualization. JJ integrated the ks_problem model to ensure reproducibility and compatibility with downstream analysis. The resulting prototype provided a reusable, well-documented solution for forecasting tasks, demonstrating depth in data management, model engineering, and artifact handling within a collaborative codebase.
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 peopleShared Repositories
7
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
Repositories Contributed To
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