KU-BIG/KUBIG_2025_FALL
Jul 2025 – Aug 2025
2 Months active
Languages Used
PythonDockerfileJupyter NotebookMarkdownShellYAML
Technical Skills
CNNComputer VisionData PreprocessingData VisualizationDeep LearningMNIST Classification
KBS
PROFILE
Kbs
Over two months, contributed a suite of end-to-end machine learning and computer vision features to the KU-BIG/KUBIG_2025_FALL repository, focusing on reproducible research and practical onboarding. Developed Jupyter Notebook tutorials for MNIST digit classification, comparing standard CNN and ResNet-like architectures, and implemented object detection with YOLOv1 on Pascal VOC. Expanded the repository with generative models such as VAE and GAN, a Vision Transformer for CIFAR-10, and a mini-CLIP with conditional diffusion experiments. Leveraged Python, PyTorch, and Docker to enable GPU-accelerated training, robust data preprocessing, and clear documentation, supporting scalable experimentation and collaborative workflows for ML practitioners.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
6
Lines of code
41,057
Activity Months2
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Work History
August 2025
15 Commits • 5 Features
Aug 1, 2025August 2025: Delivered a focused suite of end-to-end ML/vision features in KU-BIG/KUBIG_2025_FALL, enabling practical prototyping from data to evaluation on GPU. Completed multiple research-to-production style notebooks and scaffolding, with strong emphasis on reproducibility, scalable GPU training, and clear documentation to accelerate iteration and collaboration. Highlights include an end-to-end object detection demo with YOLOv1 on Pascal VOC, MNIST generative models (VAE and GAN) with GPU-accelerated training, a Vision Transformer (ViT) for CIFAR-10 with training/evaluation loops, Mini-CLIP with conditional DDPM diffusion experiments, and a robust CLAP2Diffusion project setup (Docker/Gradio) with hierarchical audio processing and extensive docs.
15 Commits • 5 Features
Aug 1, 2025August 2025: Delivered a focused suite of end-to-end ML/vision features in KU-BIG/KUBIG_2025_FALL, enabling practical prototyping from data to evaluation on GPU. Completed multiple research-to-production style notebooks and scaffolding, with strong emphasis on reproducibility, scalable GPU training, and clear documentation to accelerate iteration and collaboration. Highlights include an end-to-end object detection demo with YOLOv1 on Pascal VOC, MNIST generative models (VAE and GAN) with GPU-accelerated training, a Vision Transformer (ViT) for CIFAR-10 with training/evaluation loops, Mini-CLIP with conditional DDPM diffusion experiments, and a robust CLAP2Diffusion project setup (Docker/Gradio) with hierarchical audio processing and extensive docs.
August 2025
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for KU-BIG/KUBIG_2025_FALL: Delivered a comprehensive MNIST CNN Notebook Tutorial suite featuring a ResNet-like variant, enabling end-to-end experiments from data preprocessing to prediction visualization. The notebooks provide a side-by-side comparison of a standard CNN and a ResNet-like architecture, supporting reproducible benchmarking and rapid onboarding for data science practitioners.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for KU-BIG/KUBIG_2025_FALL: Delivered a comprehensive MNIST CNN Notebook Tutorial suite featuring a ResNet-like variant, enabling end-to-end experiments from data preprocessing to prediction visualization. The notebooks provide a side-by-side comparison of a standard CNN and a ResNet-like architecture, supporting reproducible benchmarking and rapid onboarding for data science practitioners.
Activity
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Quality Metrics
Correctness90.0%
Maintainability89.6%
Architecture89.6%
Performance86.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileJupyter NotebookMarkdownPythonShellYAML
Technical Skills
Audio ProcessingCLAPCLIPCNNClassifier-Free GuidanceComputer VisionContainerizationData PreprocessingData VisualizationDeep LearningDevOpsDiffusion ModelsDockerDocumentationGAN
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