pskcci/DX-01
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
Data ManipulationData PreprocessingData VisualizationDocumentationGRUGradient Descent
TY Oh
PROFILE
Ty Oh
During two months on the pskcci/DX-01 repository, Otation built and documented features for AR-based music learning and machine learning education. He developed a real-time fingering guide system, planned its architecture, and created comprehensive documentation to clarify technical and UX goals. Otation also implemented a Python-based factory simulation using multithreading and OpenCV for live video processing and motion detection. In parallel, he delivered Jupyter notebooks demonstrating gradient descent and RNN forecasting with Keras and NumPy, supporting reproducible ML experiments. His work emphasized repository hygiene, onboarding clarity, and end-to-end validation, reflecting a thoughtful approach to both engineering depth and maintainability.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
16Total
Bugs
1
Commits
16
Features
6
Lines of code
4,058
Activity Months2
Your Network
11 people
Work History
December 2024
8 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for pskcci/DX-01. Focused on delivering planning-grounded feature work and a demonstrator prototype, with emphasis on documentation, architecture clarity, and end-to-end video processing capabilities that enable faster future development and validation of AR-based learning workflows.
8 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for pskcci/DX-01. Focused on delivering planning-grounded feature work and a demonstrator prototype, with emphasis on documentation, architecture clarity, and end-to-end video processing capabilities that enable faster future development and validation of AR-based learning workflows.
December 2024
November 2024
8 Commits • 4 Features
Nov 1, 2024November 2024 — DX-01: Key features delivered and technical improvements focused on learning resources, reproducibility, and repository hygiene. Documentation updates, gradient descent notebooks, RNN forecasting notebook, and educational scripts were delivered. Major bug fix included removal of notebook checkpoint files to clean the repo. Overall impact: faster onboarding, clearer ML demonstrations, reproducible experiments, and cleaner version history. Technologies harnessed include Python, Jupyter notebooks, NumPy, PIL, OpenCV, and ML tools (gradient descent visualization, SimpleRNN/GRU/LSTM).
November 2024
8 Commits • 4 Features
Nov 1, 2024November 2024 — DX-01: Key features delivered and technical improvements focused on learning resources, reproducibility, and repository hygiene. Documentation updates, gradient descent notebooks, RNN forecasting notebook, and educational scripts were delivered. Major bug fix included removal of notebook checkpoint files to clean the repo. Overall impact: faster onboarding, clearer ML demonstrations, reproducible experiments, and cleaner version history. Technologies harnessed include Python, Jupyter notebooks, NumPy, PIL, OpenCV, and ML tools (gradient descent visualization, SimpleRNN/GRU/LSTM).
Activity
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Quality Metrics
Correctness86.2%
Maintainability86.2%
Architecture86.2%
Performance85.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
AR DevelopmentComputer VisionData ManipulationData PreprocessingData VisualizationDocumentationGRUGradient DescentImage ProcessingKerasLSTMMachine LearningMatplotlibMultithreadingMusic Technology
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline