KU-BIG/KUBIG_2025_FALL
Jul 2025 – Jul 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Computer VisionData VisualizationDeep LearningMachine LearningMatplotlibNumPy
Honnelove
PROFILE
Honnelove
Developed a reusable visualization script for the KU-BIG/KUBIG_2025_FALL repository, focusing on evaluating convolutional neural networks trained on the MNIST dataset. The script, implemented in Python using PyTorch, NumPy, and Matplotlib, displays random predictions alongside ground truth labels, streamlining model validation and debugging. By automating the visualization of model outputs, the tool accelerates quality assurance feedback and enhances communication of model behavior to stakeholders. The approach leveraged computer vision and data visualization techniques within a Jupyter Notebook environment, emphasizing reproducibility and ease of integration into existing workflows. No major bugs were addressed during this period, with efforts centered on feature delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,625
Activity Months1
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Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for KU-BIG/KUBIG_2025_FALL. Key feature delivered: MNIST CNN Prediction Visualization Script that visualizes random predictions from a CNN trained on MNIST, displaying ground truth and predicted labels to aid evaluation and debugging. No major bugs fixed this month. Overall impact: provides a lightweight, reusable visualization tool to accelerate model validation, improve QA feedback loops, and communicate model behavior to stakeholders. Technologies demonstrated: Python scripting, data visualization, CNN evaluation workflows, and Git-based versioning.
1 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for KU-BIG/KUBIG_2025_FALL. Key feature delivered: MNIST CNN Prediction Visualization Script that visualizes random predictions from a CNN trained on MNIST, displaying ground truth and predicted labels to aid evaluation and debugging. No major bugs fixed this month. Overall impact: provides a lightweight, reusable visualization tool to accelerate model validation, improve QA feedback loops, and communicate model behavior to stakeholders. Technologies demonstrated: Python scripting, data visualization, CNN evaluation workflows, and Git-based versioning.
July 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Computer VisionData VisualizationDeep LearningMachine LearningMatplotlibNumPyPyTorchRandom Sampling
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