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
During June 2025, Grégoire Eymard developed an advanced neural network pipeline for the blackSwanCS/Higgs_collaboration_B repository, focusing on robust model experimentation and evaluation. He implemented a tunable architecture using Python and TensorFlow, integrating hyperparameter optimization with Keras Tuner and Optuna to streamline model selection. To improve training stability, he added BatchNormalization, Dropout, and EarlyStopping, while expanding the network’s dense layers for greater capacity. The workflow now supports validation data and loss-curve visualization, enhancing scientific rigor. Grégoire also refined the preprocessing pipeline by removing redundant scaler artifacts and maintained repository hygiene, demonstrating depth in both engineering and project organization.
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 people
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
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