root-project/root
Mar 2026 – Mar 2026
1 Month active
Languages Used
C++
Technical Skills
C++C++ developmentC++ programmingalgorithm developmentcode formattingmachine learning
Neeraj
PROFILE
Neeraj
Worked on the root-project/root repository to enhance the ELU activation function for machine learning applications using C++. Addressed a critical operator precedence bug in the ELU formula, ensuring correct computation when the alpha parameter differs from the default, which improved model accuracy and reliability. Expanded test coverage by adding regression tests for non-default alpha values and integrated ONNX reference outputs, reducing the risk of silent miscomputations. Focused on code readability and maintainability by refactoring relevant C++ files and strengthening code generation reliability. Applied skills in C++ development, algorithm design, and software testing to deliver robust improvements for ML deployment scenarios.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
89
Activity Months1
Your Network
96 peopleShared Repositories
96
Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for root-project/root: Delivered critical ELU activation improvements including a correctness fix and regression tests, strengthening model accuracy, reliability, and code quality. Implemented a non-default alpha regression path (alpha=0.5) with test coverage and ONNX reference output, reducing risk of silent miscomputations. Overall, improved codegen reliability, maintainability, and business value for ML deployments.
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for root-project/root: Delivered critical ELU activation improvements including a correctness fix and regression tests, strengthening model accuracy, reliability, and code quality. Implemented a non-default alpha regression path (alpha=0.5) with test coverage and ONNX reference output, reducing risk of silent miscomputations. Overall, improved codegen reliability, maintainability, and business value for ML deployments.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ developmentC++ programmingalgorithm developmentcode formattingmachine learningnumerical methodssoftware maintenancetesting
Repositories Contributed To
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