Fernando-JAL/Neurociencias-2025-2
May 2025 – May 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Academic WritingCNNData AnalysisData PreprocessingData VisualizationDeep Learning
Alissongm04
PROFILE
Alissongm04
Contributed to the Neurociencias-2025-2 repository by delivering three core features over one month, focusing on machine learning education and reproducible workflows. Developed comprehensive ML Theory Q&A documentation in Markdown, covering foundational topics such as supervised versus unsupervised learning and model evaluation. Built a brain tumor imaging project that included data preprocessing, exploratory data analysis, and comparative modeling using CNNs, MobileNetV2, SVM, KNN, and Random Forest. Enhanced code readability and maintainability through systematic notebook formatting and annotated Python scripts. Leveraged Python, TensorFlow, and Scikit-learn to create shareable resources and maintain a clean, collaborative codebase for future academic use.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
3
Lines of code
7,843
Activity Months1
Your Network
37 peopleSame Organization
@comunidad.unam.mx17
Work History
May 2025
8 Commits • 3 Features
May 1, 2025Month: 2025-05 — Fernando-JAL/Neurociencias-2025-2 delivered three major assets: (1) ML Theory Q&A Documentation (Markdown) covering supervised vs unsupervised learning, classification vs regression, overfitting/underfitting, evaluation metrics, and neural network components; (2) Brain Tumor Imaging Project and Exam Materials including data preprocessing, exploratory data analysis, model comparisons (CNNs, MobileNetV2, SVM, KNN, Random Forest), and practical exam materials plus related image assets; (3) Code Cleanup and Notebook Formatting to improve readability with Python script annotations, output formatting refactors, and notebook section markers. No critical bugs reported this month; focus was on feature delivery and code quality. Business value includes ready-to-share learning resources, reproducible ML workflows, and a maintainable codebase for future work. Technologies demonstrated include Python, notebook-based experiments, Markdown documentation, ML model evaluation across CNNs and classical algorithms, and Git-based version control.
8 Commits • 3 Features
May 1, 2025Month: 2025-05 — Fernando-JAL/Neurociencias-2025-2 delivered three major assets: (1) ML Theory Q&A Documentation (Markdown) covering supervised vs unsupervised learning, classification vs regression, overfitting/underfitting, evaluation metrics, and neural network components; (2) Brain Tumor Imaging Project and Exam Materials including data preprocessing, exploratory data analysis, model comparisons (CNNs, MobileNetV2, SVM, KNN, Random Forest), and practical exam materials plus related image assets; (3) Code Cleanup and Notebook Formatting to improve readability with Python script annotations, output formatting refactors, and notebook section markers. No critical bugs reported this month; focus was on feature delivery and code quality. Business value includes ready-to-share learning resources, reproducible ML workflows, and a maintainable codebase for future work. Technologies demonstrated include Python, notebook-based experiments, Markdown documentation, ML model evaluation across CNNs and classical algorithms, and Git-based version control.
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability87.4%
Architecture90.0%
Performance82.6%
AI Usage22.4%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Academic WritingCNNData AnalysisData PreprocessingData VisualizationDeep LearningExploratory Data AnalysisImage ClassificationImage ProcessingKerasMachine LearningMatplotlibPandasScikit-learnTensorFlow
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