dsu-cs/csc702_fall2025
Sep 2025 – Sep 2025
1 Month active
Languages Used
HTMLMarkdownPython
Technical Skills
Content AdditionData AnalysisData PreprocessingDeep LearningDocumentationFile I/O
Tanner-ui
PROFILE
Tanner-ui
Tanner Allen developed a suite of end-to-end NLP and machine learning features for the dsu-cs/csc702_fall2025 repository, focusing on model optimization, translation, and text analytics. He implemented an Optuna-driven hyperparameter tuning workflow for Fashion-MNIST classifiers using Python and PyTorch, introducing both MLP and CNN architectures. Tanner also built a sequence-to-sequence English-to-Shakespeare translation model, integrating SentencePiece tokenization and GloVe embeddings for robust preprocessing and training. Additionally, he delivered a word embedding arithmetic toolkit and a Bag-of-Words plus TF-IDF pipeline for fake news classification. His work emphasized reproducibility, maintainable experimentation, and comprehensive documentation throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
5
Lines of code
82,787
Activity Months1
Your Network
25 peopleSame Organization
@trojans.dsu.edu5
Work History
September 2025
16 Commits • 5 Features
Sep 1, 2025September 2025 performance summary for dsu-cs/csc702_fall2025: Delivered a cohesive set of end-to-end NLP/ML features and project scaffolding, driving model optimization, translation capabilities, text analytics tooling, and reproducible workflows. The work emphasizes business value through improved model performance, expressive modeling options, and maintainable experimentation pipelines.
16 Commits • 5 Features
Sep 1, 2025September 2025 performance summary for dsu-cs/csc702_fall2025: Delivered a cohesive set of end-to-end NLP/ML features and project scaffolding, driving model optimization, translation capabilities, text analytics tooling, and reproducible workflows. The work emphasizes business value through improved model performance, expressive modeling options, and maintainable experimentation pipelines.
September 2025
Activity
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Quality Metrics
Correctness89.4%
Maintainability87.4%
Architecture84.4%
Performance81.8%
AI Usage30.0%
Skills & Technologies
Programming Languages
HTMLMarkdownPython
Technical Skills
Content AdditionData AnalysisData PreprocessingDeep LearningDocumentationFile I/OGloVeHyperparameter TuningMachine LearningModel EvaluationModel TrainingNatural Language ProcessingOptunaPandasPyTorch
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