keras-team/keras-io
Sep 2025 – Sep 2025
1 Month active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
Code RefactoringData PreprocessingData VisualizationDeep LearningDocumentationKeras
Harshith Kulkarni
PROFILE
Harshith Kulkarni
During September 2025, Harshith contributed to the keras-team/keras-io repository by developing an end-to-end Deep Learning Recommendation Model (DLRM) tutorial using KerasRS on the MovieLens 100K dataset. He implemented data preprocessing and model construction with embedding layers and DotInteraction, providing both a Python script and a Jupyter notebook for training and evaluation. Harshith also enhanced feature interaction visualization and improved documentation by fixing local image path references across Python, Jupyter Notebook, and Markdown files. His work focused on reproducibility and onboarding, leveraging skills in Python, TensorFlow, and data visualization to deliver a comprehensive, runnable example for recommender systems.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
2,748
Activity Months1
Your Network
4183 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 summary for keras-io: Delivered an end-to-end DLRM Tutorial/Example (KerasRS) on MovieLens 100K, including a Python script, Jupyter notebook, data preprocessing, embedding-based model with DotInteraction, and feature-interaction visualization. Fixed local image paths for the DLRM architecture diagram to improve offline reproducibility. These deliverables improve onboarding, reproducibility, and showcase KerasRS capabilities for recommender systems.
2 Commits • 1 Features
Sep 1, 2025September 2025 summary for keras-io: Delivered an end-to-end DLRM Tutorial/Example (KerasRS) on MovieLens 100K, including a Python script, Jupyter notebook, data preprocessing, embedding-based model with DotInteraction, and feature-interaction visualization. Fixed local image paths for the DLRM architecture diagram to improve offline reproducibility. These deliverables improve onboarding, reproducibility, and showcase KerasRS capabilities for recommender systems.
September 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
Code RefactoringData PreprocessingData VisualizationDeep LearningDocumentationKerasMachine LearningModel TrainingRecommendation SystemsTensorFlow
Repositories Contributed To
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