LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
Jupyter NotebookPythonJavaScript
Technical Skills
Computer VisionData CleaningData PreprocessingData TransformationData VisualizationDimensionality Reduction
Hutesh
PROFILE
Hutesh
Vamsi Maradani contributed to the LCIT-AISC-T3-S25/Group1 repository by developing and refining machine learning pipelines for both natural language and computer vision tasks. He built a BiLSTM-based tweet sentiment classifier with robust preprocessing and evaluation, and implemented a PCA-enhanced KNN image classification workflow to improve efficiency. Vamsi also delivered a Retrieval-Augmented Generation system for biomedical question answering using Llama-2 and FAISS, and trained a Latent Diffusion Model for image generation from Yelp reviews. His work, primarily in Python and PyTorch, emphasized reproducibility, modular data cleaning, and model evaluation, demonstrating depth in deep learning, data engineering, and NLP integration.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
7
Lines of code
161,901
Activity Months3
Your Network
7 people
Work History
July 2025
6 Commits • 3 Features
Jul 1, 2025Month: 2025-07 — concise monthly summary focusing on key accomplishments, business value, and technical milestones for LCIT-AISC-T3-S25/Group1.
6 Commits • 3 Features
Jul 1, 2025Month: 2025-07 — concise monthly summary focusing on key accomplishments, business value, and technical milestones for LCIT-AISC-T3-S25/Group1.
July 2025
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary focusing on key accomplishments and business value. This period delivered two major ML features in LCIT-AISC-T3-S25/Group1 that enable faster, data-driven decision making: (1) Tweet Sentiment Analysis BiLSTM with an embedding layer, including preprocessing steps (tokenization, vocabulary building, sequence padding) and evaluation using confusion matrices, F1 scores, and ROC curves across sentiment classes; (2) Menu Item Image Classification with EfficientNet fine-tuning, featuring unfreezing of the last five layers for improved generalization and strong performance across food, drink, and menu classes. No major bugs fixed this period. Overall impact includes enhanced social listening capabilities and improved visual item recognition for catalogs and recommendations, driving better customer insights and streamlined operations. Technologies/skills demonstrated include deep learning (BiLSTM, embeddings, preprocessing), model evaluation (confusion matrices, F1, ROC), transfer learning, and EfficientNet fine-tuning with selective layer unfreezing.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary focusing on key accomplishments and business value. This period delivered two major ML features in LCIT-AISC-T3-S25/Group1 that enable faster, data-driven decision making: (1) Tweet Sentiment Analysis BiLSTM with an embedding layer, including preprocessing steps (tokenization, vocabulary building, sequence padding) and evaluation using confusion matrices, F1 scores, and ROC curves across sentiment classes; (2) Menu Item Image Classification with EfficientNet fine-tuning, featuring unfreezing of the last five layers for improved generalization and strong performance across food, drink, and menu classes. No major bugs fixed this period. Overall impact includes enhanced social listening capabilities and improved visual item recognition for catalogs and recommendations, driving better customer insights and streamlined operations. Technologies/skills demonstrated include deep learning (BiLSTM, embeddings, preprocessing), model evaluation (confusion matrices, F1, ROC), transfer learning, and EfficientNet fine-tuning with selective layer unfreezing.
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered data cleaning and ML pipeline enhancements, establishing reusable data processing and model evaluation workflows. No critical bugs reported this month; focus was on delivering tangible data quality improvements and a scalable ML evaluation path that supports faster iteration and clearer traceability.
2 Commits • 2 Features
May 1, 2025May 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered data cleaning and ML pipeline enhancements, establishing reusable data processing and model evaluation workflows. No critical bugs reported this month; focus was on delivering tangible data quality improvements and a scalable ML evaluation path that supports faster iteration and clearer traceability.
May 2025
Activity
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Quality Metrics
Correctness78.0%
Maintainability74.0%
Architecture77.0%
Performance70.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
JavaScriptJupyter NotebookPython
Technical Skills
Computer VisionData CleaningData EngineeringData PreprocessingData ScienceData TransformationData VisualizationDeep LearningDiffusersDimensionality ReductionGenerative ModelsHugging Face TransformersImage ClassificationK-Nearest NeighborsLIME
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