blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data PreprocessingData ScienceDeep LearningHyperparameter OptimizationKerasMachine Learning
Gregoire13
PROFILE
Gregoire13
Developed an advanced neural network pipeline for the blackSwanCS/Higgs_collaboration_B repository, focusing on tunability and robust evaluation for scientific experimentation. Leveraged Python, TensorFlow, and Keras to implement hyperparameter optimization using both Keras Tuner and Optuna, while enhancing training stability through BatchNormalization, Dropout, and EarlyStopping techniques. Expanded the model’s architecture with additional dense layers and integrated validation data support alongside loss curve visualization to facilitate thorough model assessment. Streamlined the preprocessing and model saving workflow by removing an obsolete scaler artifact, and maintained repository hygiene with a minor housekeeping commit, ensuring the project’s readiness for efficient experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
582
Activity Months1
Your Network
41 peopleShared Repositories
21
Work History
June 2025
4 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for blackSwanCS/Higgs_collaboration_B: Delivered an advanced, tunable neural network pipeline with hyperparameter optimization and training stability enhancements, ready for robust evaluation on the blackSwan dataset. Implemented hyperparameter optimization via Keras Tuner and Optuna; improved training stability with BatchNormalization, Dropout, and EarlyStopping; expanded dense layers; enabled validation data and plots of loss curves. Removed a scaler artifact and included a minor housekeeping commit to reflect repository hygiene. Prepared the project for efficient experimentation and reliable scientific evaluation.
4 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for blackSwanCS/Higgs_collaboration_B: Delivered an advanced, tunable neural network pipeline with hyperparameter optimization and training stability enhancements, ready for robust evaluation on the blackSwan dataset. Implemented hyperparameter optimization via Keras Tuner and Optuna; improved training stability with BatchNormalization, Dropout, and EarlyStopping; expanded dense layers; enabled validation data and plots of loss curves. Removed a scaler artifact and included a minor housekeeping commit to reflect repository hygiene. Prepared the project for efficient experimentation and reliable scientific evaluation.
June 2025
Activity
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Quality Metrics
Correctness65.0%
Maintainability65.0%
Architecture65.0%
Performance55.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data PreprocessingData ScienceDeep LearningHyperparameter OptimizationKerasMachine LearningModel EvaluationModel TrainingPythonTensorFlow
Repositories Contributed To
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