racousin/data_science_practice_2024
Feb 2025 – Feb 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingData ScienceData VisualizationDeep LearningGymnasiumMachine Learning
jules.robin3005@orange.fr
PROFILE
Jules.robin3005@orange.fr
During February 2025, this developer contributed to the racousin/data_science_practice_2024 repository by building two core machine learning features. They developed a FrozenLake Q-learning experiment notebook using Python and Gymnasium, implementing an Agent class and utilities for training and evaluation to support reproducible reinforcement learning workflows. Additionally, they created an MNIST digit classifier in PyTorch, enhancing the model with dropout and early stopping to improve accuracy and robustness. Their work emphasized reproducible pipelines and sustainable development practices, enabling faster experimentation and clearer evaluation. Throughout the month, they focused on practical data science, deep learning, and data preprocessing techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
11,224
Activity Months1
Your Network
15 peopleShared Repositories
15
Work History
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 Monthly Summary for developer work in racousin/data_science_practice_2024. Focused on delivering practical ML experimentation and reproducible evaluation pipelines. Key features delivered include a FrozenLake Q-learning Experiment Notebook and an improved MNIST classifier with dropout and early stopping. No major bugs reported this month; minor notebook cleanups and environment refinements were performed to support sustainable development. This work enhances business value by enabling faster experimentation cycles, clearer evaluation, and more robust models for real-world tasks.
2 Commits • 2 Features
Feb 1, 2025February 2025 Monthly Summary for developer work in racousin/data_science_practice_2024. Focused on delivering practical ML experimentation and reproducible evaluation pipelines. Key features delivered include a FrozenLake Q-learning Experiment Notebook and an improved MNIST classifier with dropout and early stopping. No major bugs reported this month; minor notebook cleanups and environment refinements were performed to support sustainable development. This work enhances business value by enabling faster experimentation cycles, clearer evaluation, and more robust models for real-world tasks.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingData ScienceData VisualizationDeep LearningGymnasiumMachine LearningPyTorchPythonQ-LearningReinforcement Learning
Repositories Contributed To
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