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
Developed comprehensive Lab 7 materials for the umnooob/course-demo repository, focusing on meta-learning and transfer learning concepts. Delivered a detailed Jupyter Notebook featuring baseline transfer learning and MAML meta-learning implementations using the Omniglot dataset, accompanied by supporting images and Markdown documentation. Leveraged Python and PyTorch to create reproducible experiments, enabling students and instructors to efficiently run and evaluate machine learning workflows. The work emphasized clarity in experimental setup and usage, enhancing curriculum readiness for machine learning labs. By providing accessible, well-documented resources, this contribution accelerated the deployment and evaluation of few-shot learning techniques in educational settings.
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
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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
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