umnooob/course-demo
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ScienceDeep LearningFew-Shot LearningMachine LearningMeta LearningPyTorch
fyc
PROFILE
Fyc
During April 2025, this developer contributed to the umnooob/course-demo repository by delivering Lab 7 materials focused on meta-learning and transfer learning. They created a comprehensive Jupyter Notebook that implemented both baseline transfer learning and MAML meta-learning algorithms on the Omniglot dataset, supporting reproducible experimentation for students and instructors. The work included detailed Markdown documentation and supporting images to clarify concepts and experimental setup, ensuring the materials were accessible and ready for curriculum integration. Utilizing Python, PyTorch, and Jupyter Notebook, the developer demonstrated depth in machine learning and meta-learning, producing well-documented resources that facilitate hands-on learning and evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,380
Activity Months1
Your Network
7 peopleShared Repositories
7
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — umnooob/course-demo Key deliverables: - Lab 7 materials for meta-learning and transfer learning: a comprehensive Jupyter Notebook with baseline transfer learning and MAML meta-learning implementations on Omniglot, plus images and Markdown docs detailing concepts, experimental setup, and usage. Impact: - Provides students and instructors with ready-to-run materials to reproduce experiments, accelerating learning, evaluation, and curriculum deployment. Notes: - Commit for this work: a9916f9e631fc8c8748b87de9b0932579976d63d (feat: add lab7 materials). Technologies/skills demonstrated: - Python, Jupyter Notebook, ML concepts (transfer learning, MAML), Omniglot dataset; Markdown documentation for reproducibility.
1 Commits • 1 Features
Apr 1, 2025April 2025 — umnooob/course-demo Key deliverables: - Lab 7 materials for meta-learning and transfer learning: a comprehensive Jupyter Notebook with baseline transfer learning and MAML meta-learning implementations on Omniglot, plus images and Markdown docs detailing concepts, experimental setup, and usage. Impact: - Provides students and instructors with ready-to-run materials to reproduce experiments, accelerating learning, evaluation, and curriculum deployment. Notes: - Commit for this work: a9916f9e631fc8c8748b87de9b0932579976d63d (feat: add lab7 materials). Technologies/skills demonstrated: - Python, Jupyter Notebook, ML concepts (transfer learning, MAML), Omniglot dataset; Markdown documentation for reproducibility.
April 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ScienceDeep LearningFew-Shot LearningMachine LearningMeta LearningPyTorchTransfer Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline