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
In July 2025, KC Lee 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. KC Lee implemented the solution using Python and Jupyter Notebook, leveraging libraries such as PyTorch, NumPy, and Matplotlib for data handling and visualization. While the work focused on a single feature, it addressed a common need in computer vision workflows by providing a lightweight tool to accelerate model validation and improve stakeholder communication.
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
1093 peopleShared Repositories
28
seohyeon0508Member
yooncy1122Member
elishaMember
eunseo5703Member
Haeune-JeonMember
Nam SubinMember
hwang10-22Member
전하은Member
Junhee-Park0Member
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline