LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
KerasMarkdownPythonJupyter Notebook
Technical Skills
Data AnalysisData VisualizationDeep LearningHyperparameter TuningMachine LearningModel Interpretability
Chalithya
PROFILE
Chalithya
Over three months, contributed to the LCIT-AISC-T3-S25/Group1 repository by building and refining machine learning pipelines for NLP, computer vision, and generative modeling. Developed text analytics and sentiment analysis workflows using Python, Keras, and NLTK, emphasizing robust preprocessing, model tuning, and interpretability with LIME. Enhanced CNN and BiGRU models for transparency and stakeholder trust, and implemented a Word2Vec-based non-causal Transformer and a Latent Diffusion Model with cross-attention for advanced experimentation. Delivered a RAG evaluation framework for medical chatbots, maintained code quality through targeted cleanups, and ensured reproducibility and maintainability across Jupyter Notebooks and collaborative group projects.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
10Total
Bugs
2
Commits
10
Features
7
Lines of code
156,240
Activity Months3
Your Network
7 peopleWork History
July 2025
5 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Focused on building scalable ML experimentation pipelines, evaluation tooling, and repository hygiene to accelerate business value. Delivered end-to-end model experimentation capabilities with Word2Vec-based non-causal Transformer (tuning and training/evaluation pipelines), a comprehensive RAG evaluation framework with medical chatbot integration and configuration comparisons (temperature, max tokens), and a Latent Diffusion Model tuning notebook with cross-attention for improved image generation. Conducted targeted maintenance to remove non-functional production lines in notebooks and archived older assignments for clarity and future reference. Achievements emphasize measurable improvements in model tuning efficiency, evaluation rigor, and repository organization, aligning technical work with business outcomes.
5 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Focused on building scalable ML experimentation pipelines, evaluation tooling, and repository hygiene to accelerate business value. Delivered end-to-end model experimentation capabilities with Word2Vec-based non-causal Transformer (tuning and training/evaluation pipelines), a comprehensive RAG evaluation framework with medical chatbot integration and configuration comparisons (temperature, max tokens), and a Latent Diffusion Model tuning notebook with cross-attention for improved image generation. Conducted targeted maintenance to remove non-functional production lines in notebooks and archived older assignments for clarity and future reference. Achievements emphasize measurable improvements in model tuning efficiency, evaluation rigor, and repository organization, aligning technical work with business outcomes.
July 2025
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for LCIT-AISC-T3-S25/Group1: Delivered feature enhancements and interpretability for NLP and CV models, with a clear focus on business value and reliability. NLP Sentiment Analysis: BiGRU model tuning with extensive preprocessing (sampling, cleaning, negation handling, tokenization) and evaluation; added LIME-based global interpretation for model insights. CV: three tuning iterations emphasizing class weights and early stopping, with LIME explainability added for the best model (Model 3). These efforts increased trust in predictions and provided actionable model insights for stakeholders, while keeping the codebase maintainable and well-documented.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for LCIT-AISC-T3-S25/Group1: Delivered feature enhancements and interpretability for NLP and CV models, with a clear focus on business value and reliability. NLP Sentiment Analysis: BiGRU model tuning with extensive preprocessing (sampling, cleaning, negation handling, tokenization) and evaluation; added LIME-based global interpretation for model insights. CV: three tuning iterations emphasizing class weights and early stopping, with LIME explainability added for the best model (Model 3). These efforts increased trust in predictions and provided actionable model insights for stakeholders, while keeping the codebase maintainable and well-documented.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 performance highlights for LCIT-AISC-T3-S25/Group1 include two major feature deliveries that drive data insight and model explainability, with robust error handling and deployment-ready artifacts. The work directly supports business value by enabling comprehensive text analytics on CSV datasets and providing transparent CNN model decisions for stakeholders.
3 Commits • 2 Features
May 1, 2025May 2025 performance highlights for LCIT-AISC-T3-S25/Group1 include two major feature deliveries that drive data insight and model explainability, with robust error handling and deployment-ready artifacts. The work directly supports business value by enabling comprehensive text analytics on CSV datasets and providing transparent CNN model decisions for stakeholders.
May 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability82.0%
Architecture84.0%
Performance80.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
Jupyter NotebookKerasMarkdownPython
Technical Skills
Computer VisionData AnalysisData PreprocessingData VisualizationDeep LearningDiffusion ModelsExplainable AI (XAI)FAISSHugging FaceHyperparameter TuningJupyter NotebookKerasLangChainLimeMachine Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline