m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPythonText
Technical Skills
Data ManagementData PreprocessingData ScienceData VisualizationJupyter NotebookMachine Learning
JRowbottomGit
PROFILE
Jrowbottomgit
Developed an end-to-end Kolmogorov–Sinai (KS) workflow for the ML4DE_hackathon repository, focusing on time-series prediction using PyTorch and Jupyter Notebook. The work included implementing a training script for neural networks, visualizing Lorenz system predictions, and persisting model artifacts to support reproducibility and deployment. Enhanced the workflow with streamlined notebook tooling for easier experimentation and result capture, and introduced plots comparing predicted versus true states to facilitate rapid validation. Additionally, updated the Team5 roster to reflect current membership, improving collaboration and traceability. The contributions emphasized data management, model training, and time series analysis within a collaborative data science environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
3,804
Activity Months1
Your Network
7 peopleShared Repositories
7
Work History
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025: Delivered end-to-end KS workflow in the ML4DE_hackathon repo with PyTorch time-series training, Lorenz system prediction visualizations, a KS notebook, and model artifact persistence. Updated Team5 roster to replace placeholders with actual names. This work improved reproducibility, collaboration, and ready-to-deploy artifacts while enhancing visibility into team composition.
7 Commits • 2 Features
Apr 1, 2025April 2025: Delivered end-to-end KS workflow in the ML4DE_hackathon repo with PyTorch time-series training, Lorenz system prediction visualizations, a KS notebook, and model artifact persistence. Updated Team5 roster to replace placeholders with actual names. This work improved reproducibility, collaboration, and ready-to-deploy artifacts while enhancing visibility into team composition.
April 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage25.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonText
Technical Skills
Data ManagementData PreprocessingData ScienceData VisualizationJupyter NotebookMachine LearningModel TrainingNeural NetworksPyTorchRK4 IntegrationTime Series Analysis
Repositories Contributed To
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