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
During July 2025, Kyeongchan Choi developed a reusable MNIST CNN Prediction Visualization Script for the KU-BIG/KUBIG_2025_FALL repository. The script enables users to visualize random predictions from a convolutional neural network trained on MNIST, displaying both ground truth and predicted labels to streamline model evaluation and debugging. Built using Python and Jupyter Notebook, the tool leverages PyTorch for model inference, NumPy for data handling, and Matplotlib for visualization. Choi’s work focused on improving the model validation process and facilitating clearer communication of model behavior to stakeholders. The contribution demonstrated solid application of computer vision and data visualization techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,625
Activity Months1
Your Network
1028 people
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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