KU-BIG/KUBIG_2025_FALL
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Computer VisionData VisualizationDeep LearningMachine LearningPyTorch
elisha
PROFILE
Elisha
Elisha developed an end-to-end convolutional neural network pipeline for MNIST digit classification in the KU-BIG/KUBIG_2025_FALL repository. The project encompassed data preprocessing, model training, evaluation, and visualization of training metrics, all implemented in Python using PyTorch. By integrating data visualization and deep learning techniques, Elisha enabled clear monitoring of model convergence and performance throughout the workflow. The resulting pipeline improved digit recognition capabilities and provided a reusable framework for rapid prototyping and validation of machine learning models. The work demonstrated depth in computer vision and machine learning, with a focus on maintainability and data-driven model selection without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
456,095
Activity Months1
Your Network
27 people
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 (2025-12) - KU-BIG/KUBIG_2025_FALL: Delivered end-to-end CNN-based MNIST digit classification pipeline. Key features included data preprocessing, model training, evaluation, and visualization of training metrics. No major bugs reported; the work completed improved training visibility and provides a reusable ML workflow for rapid prototyping. Impact: enhanced digit recognition capability and faster iteration cycles, enabling data-driven validation of model choices. Technologies: Python, CNN architectures, end-to-end ML pipeline, data visualization, version-controlled with commit 6f1421227a16f9263baf4b0bbbee977e8550aac0; repository references KU-BIG/KUBIG_2025_FALL.
1 Commits • 1 Features
Dec 1, 2025December 2025 (2025-12) - KU-BIG/KUBIG_2025_FALL: Delivered end-to-end CNN-based MNIST digit classification pipeline. Key features included data preprocessing, model training, evaluation, and visualization of training metrics. No major bugs reported; the work completed improved training visibility and provides a reusable ML workflow for rapid prototyping. Impact: enhanced digit recognition capability and faster iteration cycles, enabling data-driven validation of model choices. Technologies: Python, CNN architectures, end-to-end ML pipeline, data visualization, version-controlled with commit 6f1421227a16f9263baf4b0bbbee977e8550aac0; repository references KU-BIG/KUBIG_2025_FALL.
December 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionData VisualizationDeep LearningMachine LearningPyTorch
Repositories Contributed To
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