Artelnics/opennn
Feb 2025 – Mar 2025
2 Months active
Languages Used
C++
Technical Skills
C++C++ DevelopmentData PreprocessingMachine LearningNeural NetworksNumerical Methods
IreneFigueruelo
PROFILE
Irenefigueruelo
Contributed to the Artelnics/opennn repository by delivering targeted improvements to neural network reliability and testing infrastructure over a two-month period. Focused on C++ development, the work included correcting min-max scaling logic, expanding and refining unit tests for core components such as GeneticAlgorithm and NeuralNetwork, and implementing Hessian-based optimization support. Emphasized robust error handling, precise assertions, and comprehensive test coverage to reduce regression risk and improve maintainability. Addressed issues in forward propagation, scaling, and statistical evaluation, ensuring accurate model training and evaluation. Applied skills in numerical methods, debugging, and software testing to enhance reproducibility and integration for downstream machine learning workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
14Total
Bugs
4
Commits
14
Features
4
Lines of code
2,732
Activity Months2
Your Network
19 peopleWork History
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for Artelnics/opennn. Delivered targeted reliability and accuracy improvements across testing, optimization, and core components. Key enhancements include Hessian-based optimization support, expanded unit tests for GeneticAlgorithm and NeuralNetwork, and robust testing around forward propagation and scaling, driving higher confidence in model evaluation and optimization workflows. Addressed test naming and robustness issues to reduce CI noise and regression risk. These updates improve maintainability, reduce debugging time, and strengthen the library's capability to support more reliable neural network training and evaluation.
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for Artelnics/opennn. Delivered targeted reliability and accuracy improvements across testing, optimization, and core components. Key enhancements include Hessian-based optimization support, expanded unit tests for GeneticAlgorithm and NeuralNetwork, and robust testing around forward propagation and scaling, driving higher confidence in model evaluation and optimization workflows. Addressed test naming and robustness issues to reduce CI noise and regression risk. These updates improve maintainability, reduce debugging time, and strengthen the library's capability to support more reliable neural network training and evaluation.
March 2025
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for Artelnics/opennn. Delivered code quality improvements and corrected foundational mathematical operations, with a focus on test coverage, reliability, and maintainability in the open NN library.
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for Artelnics/opennn. Delivered code quality improvements and corrected foundational mathematical operations, with a focus on test coverage, reliability, and maintainability in the open NN library.
Activity
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Quality Metrics
Correctness83.6%
Maintainability80.6%
Architecture72.8%
Performance68.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm TestingC++C++ DevelopmentData AnalysisData PreprocessingDebuggingLinear AlgebraMachine LearningNeural NetworksNumerical AnalysisNumerical MethodsRefactoringSoftware DevelopmentSoftware TestingTesting
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