kgkorchamhrd/intel-03
Feb 2025 – Mar 2025
2 Months active
Languages Used
MarkdownPython
Technical Skills
Code CleanupData ManipulationData VisualizationDeep LearningDocumentationFile Management
buskingsue
PROFILE
Buskingsue
During two months on the kgkorchamhrd/intel-03 repository, abcdzip@naver.com developed a computer vision-based Rock-Paper-Scissors game using Python, MediaPipe, and OpenCV, enabling real-time hand gesture recognition and interactive gameplay. They also created a gradient descent visualization suite and implemented end-to-end machine learning experiments, including a Fashion MNIST classifier and a perceptron for logic gates, leveraging NumPy and TensorFlow for data manipulation and model training. Their work included extensive documentation updates, onboarding improvements, and the addition of ML training visualizations, resulting in a well-structured codebase that supports experimentation, knowledge transfer, and client-facing demonstrations without reported bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
6
Lines of code
936
Activity Months2
Your Network
1018 people
Work History
March 2025
4 Commits • 2 Features
Mar 1, 2025Month: 2025-03\n\nKey features delivered:\n- Rock-Paper-Scissors game using computer vision with MediaPipe that detects hand gestures from a webcam, plays against a computer opponent, and displays the player's gesture, computer's choice, and the result. Includes project documentation (README) and a presentation resource.\n- Visualization artifacts from ML training added: predictions.png, train_accuracy.png, train_loss.png to illustrate model performance.\n\nMajor bugs fixed: None reported this month.\n\nOverall impact and accomplishments:\n- Delivered a functional CV-based interactive demo and accompanying ML visualization assets to support demos, knowledge transfer, and stakeholder demonstrations.\n- Strengthened repository readiness for client-facing demos and internal reviews.\n\nTechnologies/Skills demonstrated:\n- Computer vision with MediaPipe; real-time gesture detection and game logic\n- ML training visualization and pipeline artifact management\n- Git-based collaboration and documentation (README, presentation materials)
4 Commits • 2 Features
Mar 1, 2025Month: 2025-03\n\nKey features delivered:\n- Rock-Paper-Scissors game using computer vision with MediaPipe that detects hand gestures from a webcam, plays against a computer opponent, and displays the player's gesture, computer's choice, and the result. Includes project documentation (README) and a presentation resource.\n- Visualization artifacts from ML training added: predictions.png, train_accuracy.png, train_loss.png to illustrate model performance.\n\nMajor bugs fixed: None reported this month.\n\nOverall impact and accomplishments:\n- Delivered a functional CV-based interactive demo and accompanying ML visualization assets to support demos, knowledge transfer, and stakeholder demonstrations.\n- Strengthened repository readiness for client-facing demos and internal reviews.\n\nTechnologies/Skills demonstrated:\n- Computer vision with MediaPipe; real-time gesture detection and game logic\n- ML training visualization and pipeline artifact management\n- Git-based collaboration and documentation (README, presentation materials)
March 2025
February 2025
10 Commits • 4 Features
Feb 1, 2025February 2025 (2025-02) focused on strengthening developer experience, expanding experimentation capabilities, and laying groundwork for ML tooling. Key outcomes include substantial documentation and scaffolding improvements, new utilities for matrix operations, a gradient descent visualization suite, and end-to-end ML experiments with Fashion MNIST and a simple perceptron. Minor maintenance tasks addressed to improve repo hygiene and onboarding efficiency.
February 2025
10 Commits • 4 Features
Feb 1, 2025February 2025 (2025-02) focused on strengthening developer experience, expanding experimentation capabilities, and laying groundwork for ML tooling. Key outcomes include substantial documentation and scaffolding improvements, new utilities for matrix operations, a gradient descent visualization suite, and end-to-end ML experiments with Fashion MNIST and a simple perceptron. Minor maintenance tasks addressed to improve repo hygiene and onboarding efficiency.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Code CleanupComputer VisionData ManipulationData VisualizationDeep LearningDocumentationFile ManagementGame DevelopmentGradient DescentMachine LearningMediaPipeNeural NetworksNumPyNumerical ComputingOpenCV
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