KU-BIG/KUBIG_2025_FALL
Jul 2025 – Aug 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
CNNComputer VisionData PreprocessingData VisualizationDeep LearningImage Classification
hwang10-22
PROFILE
Hwang10-22
Over a two-month period, contributed to the KU-BIG/KUBIG_2025_FALL repository by developing modular machine learning pipelines and generative modeling frameworks using Python and PyTorch. Built reusable Jupyter Notebooks for MNIST digit classification with CNNs and ResNet-like architectures, and implemented an end-to-end YOLOv1 object detection workflow on the Pascal VOC dataset, including GPU-optimized evaluation utilities. Developed a Vision Transformer model for CIFAR-10 and established a flexible framework for training Variational Autoencoders and Generative Adversarial Networks on MNIST. Emphasized rapid experimentation, reproducibility, and maintainable code, supporting robust model evaluation, visualization, and streamlined onboarding for future machine learning research.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
7,241
Activity Months2
Your Network
28 peopleShared Repositories
28
seohyeon0508Member
yooncy1122Member
elishaMember
eunseo5703Member
Haeune-JeonMember
Nam SubinMember
전하은Member
Junhee-Park0Member
junseokMember
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly Summary - August 2025 (KU-BIG/KUBIG_2025_FALL) Overview: Implemented a foundational MNIST-based VAE and GAN training framework, establishing a reusable, modular pipeline for generative modeling experiments. The work emphasizes business value through rapid experimentation, reproducibility, and a clear path to iterative improvements in data generation and feature learning.
1 Commits • 1 Features
Aug 1, 2025Monthly Summary - August 2025 (KU-BIG/KUBIG_2025_FALL) Overview: Implemented a foundational MNIST-based VAE and GAN training framework, establishing a reusable, modular pipeline for generative modeling experiments. The work emphasizes business value through rapid experimentation, reproducibility, and a clear path to iterative improvements in data generation and feature learning.
August 2025
July 2025
5 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on feature delivery and technical accomplishments in KU-BIG/KUBIG_2025_FALL. Delivered three major ML features with supporting maintenance work, improving experimentation speed, reproducibility, and cross-framework capabilities. Business value centers on rapid prototyping, robust evaluation, and GPU-accelerated workflows.
July 2025
5 Commits • 3 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on feature delivery and technical accomplishments in KU-BIG/KUBIG_2025_FALL. Delivered three major ML features with supporting maintenance work, improving experimentation speed, reproducibility, and cross-framework capabilities. Business value centers on rapid prototyping, robust evaluation, and GPU-accelerated workflows.
Activity
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Quality Metrics
Correctness88.4%
Maintainability86.6%
Architecture86.6%
Performance76.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
CNNComputer VisionData PreprocessingData ScienceData VisualizationDeep LearningGenerative Adversarial Networks (GAN)Generative ModelsImage ClassificationJupyter NotebookMachine LearningModel EvaluationModel TrainingObject DetectionPascal VOC Dataset
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