dsu-cs/csc702_fall2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonJupyter Notebook
Technical Skills
Data PreprocessingDeep LearningHyperparameter OptimizationMachine LearningOptunaPyTorch
Aman Singh
PROFILE
Aman Singh
During two months contributing to dsu-cs/csc702_fall2025, Asrar developed a robust machine learning infrastructure and advanced NLP pipeline. He built a comprehensive Fashion-MNIST training system, implementing data preprocessing, MLP and CNN model definitions, and reproducible benchmarking utilities using Python and PyTorch. Hyperparameter optimization was integrated via Optuna and Ray Tune, while a Random Forest baseline supported comparative analysis. In October, Asrar delivered an end-to-end Transformer-based sentiment analysis pipeline for IMDB, featuring custom tokenization, sinusoidal positional encoding, and attention visualization. He also addressed technical debt by removing deprecated components, ensuring a clean, maintainable codebase with no major bugs reported.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
15,422
Activity Months2
Your Network
20 people
Work History
October 2025
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for dsU-cs/csc702_fall2025: Delivered a production-quality end-to-end Transformer-based sentiment analysis pipeline for IMDB, enabling scalable sentiment classification research and potential product insights. The work encompassed data ingestion, tokenization, vocabulary building, model construction with sinusoidal positional encoding and Transformer encoder layers, plus training/evaluation utilities and attention visualization for interpretability. Cleaned up technical debt by removing deprecated Komal_Charles Transformer notebook and script, reducing clutter and confusion in the repository.
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for dsU-cs/csc702_fall2025: Delivered a production-quality end-to-end Transformer-based sentiment analysis pipeline for IMDB, enabling scalable sentiment classification research and potential product insights. The work encompassed data ingestion, tokenization, vocabulary building, model construction with sinusoidal positional encoding and Transformer encoder layers, plus training/evaluation utilities and attention visualization for interpretability. Cleaned up technical debt by removing deprecated Komal_Charles Transformer notebook and script, reducing clutter and confusion in the repository.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 summary focused on delivering a comprehensive Fashion-MNIST training infrastructure and baselines for dsu-cs/csc702_fall2025. Implemented data handling, model definitions (MLP and CNN), and training utilities; introduced hyperparameter optimization workflows using random search, Optuna, and Ray Tune; established a Random Forest baseline for classification and regression. Configurations and utilities were centralized to ensure reproducible experiments and scalable benchmarking. No major bugs reported this month; minor issues mitigated promptly. This work enables rapid experimentation, robust benchmarking, and educational support for ML coursework.
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 summary focused on delivering a comprehensive Fashion-MNIST training infrastructure and baselines for dsu-cs/csc702_fall2025. Implemented data handling, model definitions (MLP and CNN), and training utilities; introduced hyperparameter optimization workflows using random search, Optuna, and Ray Tune; established a Random Forest baseline for classification and regression. Configurations and utilities were centralized to ensure reproducible experiments and scalable benchmarking. No major bugs reported this month; minor issues mitigated promptly. This work enables rapid experimentation, robust benchmarking, and educational support for ML coursework.
Activity
Loading activity data...
Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingDeep LearningHugging Face TransformersHyperparameter OptimizationMachine LearningModel TrainingNatural Language ProcessingOptunaPyTorchRay TuneScikit-learnSentiment AnalysisTransformer ModelsTransformers
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline