Artelnics/opennn
Oct 2024 – Dec 2025
15 Months active
Languages Used
C++XMLJavaScriptUnknownYAMLcppPropertiesCSV
Technical Skills
C++C++ DevelopmentComputer VisionData HandlingData LoadingData Preprocessing
rubenareces
PROFILE
Rubenareces
Ruben Areces developed advanced neural network features and infrastructure for the Artelnics/opennn repository, focusing on scalable GPU-accelerated training and robust data workflows. He engineered CUDA-enabled layers, optimized memory management, and integrated cross-platform build systems using C++ and CMake, enabling efficient model training on both CPU and GPU. His work included refactoring XML-based configuration, enhancing genetic algorithm modules, and implementing parallelism with OpenMP. By improving dataset handling, serialization, and evaluation metrics, Ruben increased reproducibility and reliability across experiments. His contributions emphasized maintainable code, performance optimization, and comprehensive testing, resulting in a more extensible and production-ready deep learning library.
Overall Statistics
Feature vs Bugs
74%Features
Repository Contributions
238Total
Bugs
22
Commits
238
Features
63
Lines of code
115,116
Activity Months15
Your Network
15 peopleSame Organization
@artelnics.com1
Work History
December 2025
4 Commits • 2 Features
Dec 1, 2025December 2025 — Open Neural Networks Library (OpenNN) improvements in Artelnics/opennn focused on performance, parallelism, and maintainability. Delivered two major features with traceable commits, enhancing runtime efficiency and code quality, setting the stage for scalable growth. No critical bugs fixed this month.
4 Commits • 2 Features
Dec 1, 2025December 2025 — Open Neural Networks Library (OpenNN) improvements in Artelnics/opennn focused on performance, parallelism, and maintainability. Delivered two major features with traceable commits, enhancing runtime efficiency and code quality, setting the stage for scalable growth. No critical bugs fixed this month.
December 2025
November 2025
14 Commits • 4 Features
Nov 1, 2025November 2025 monthly summary for Artelnics/opennn: Delivered substantial feature and reliability improvements across the genetic algorithm, CUDA-based training, and testing pipelines, with a focus on business value, scalability, and reproducibility.
November 2025
14 Commits • 4 Features
Nov 1, 2025November 2025 monthly summary for Artelnics/opennn: Delivered substantial feature and reliability improvements across the genetic algorithm, CUDA-based training, and testing pipelines, with a focus on business value, scalability, and reproducibility.
October 2025
17 Commits • 5 Features
Oct 1, 2025October 2025 — Artelnics/opennn: Delivered cross-platform performance, robustness, and release-readiness improvements that enable faster experimentation and more reliable results. Highlights include Windows OpenMP build enhancements for examples/tests with dataset instantiation cleanup; robust Neural Network XML loading and serialization; data cleaning with mean-imputation for GA data prep; Genetic Algorithm enhancements with proper input constraints and broader test coverage; and codebase cleanup plus release preparation, including enabling default example builds and OpenNN v7.1.0 RC readiness.
17 Commits • 5 Features
Oct 1, 2025October 2025 — Artelnics/opennn: Delivered cross-platform performance, robustness, and release-readiness improvements that enable faster experimentation and more reliable results. Highlights include Windows OpenMP build enhancements for examples/tests with dataset instantiation cleanup; robust Neural Network XML loading and serialization; data cleaning with mean-imputation for GA data prep; Genetic Algorithm enhancements with proper input constraints and broader test coverage; and codebase cleanup plus release preparation, including enabling default example builds and OpenNN v7.1.0 RC readiness.
October 2025
September 2025
11 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for Artelnics/opennn: Delivered core features and platform improvements to advance model demos, performance, and release readiness. Key features include a melanoma cancer detection example with CUDA-enabled execution and refactoring of existing demos; CUDA memory management optimization to improve throughput and memory utilization; and build-system/platform readiness improvements (CMake/Qt enhancements, cross-platform wiring, and support for blank components). Major bugs fixed include CUDA memory error fixes and QT compilation fixes, with RC milestones to stabilize the build pipeline. Impact: expanded end-user demonstrations, improved runtime efficiency, and reduced integration risk across platforms, positioning the project for a smoother OpenNN v7.0.0 release. Technologies demonstrated: CUDA programming, memory management optimization, CMake and Qt tooling, cross-platform software engineering, and release-management practices.
September 2025
11 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for Artelnics/opennn: Delivered core features and platform improvements to advance model demos, performance, and release readiness. Key features include a melanoma cancer detection example with CUDA-enabled execution and refactoring of existing demos; CUDA memory management optimization to improve throughput and memory utilization; and build-system/platform readiness improvements (CMake/Qt enhancements, cross-platform wiring, and support for blank components). Major bugs fixed include CUDA memory error fixes and QT compilation fixes, with RC milestones to stabilize the build pipeline. Impact: expanded end-user demonstrations, improved runtime efficiency, and reduced integration risk across platforms, positioning the project for a smoother OpenNN v7.0.0 release. Technologies demonstrated: CUDA programming, memory management optimization, CMake and Qt tooling, cross-platform software engineering, and release-management practices.
August 2025
8 Commits • 2 Features
Aug 1, 2025August 2025: Delivered CUDA acceleration and cross-platform build enhancements for Artelnics/opennn, plus CPU fallback mode to enable training/testing on non-CUDA environments. Implemented a centralized reference_all_layers method and unified parameter access across CPU/CUDA, with an Iris dataset example to validate end-to-end workflow. Achieved significant build reliability improvements across Windows and Linux with QT/CUDA integration fixes. Result: higher performance on CUDA-enabled hardware, broader deployment options, and improved maintainability.
8 Commits • 2 Features
Aug 1, 2025August 2025: Delivered CUDA acceleration and cross-platform build enhancements for Artelnics/opennn, plus CPU fallback mode to enable training/testing on non-CUDA environments. Implemented a centralized reference_all_layers method and unified parameter access across CPU/CUDA, with an Iris dataset example to validate end-to-end workflow. Achieved significant build reliability improvements across Windows and Linux with QT/CUDA integration fixes. Result: higher performance on CUDA-enabled hardware, broader deployment options, and improved maintainability.
August 2025
July 2025
37 Commits • 10 Features
Jul 1, 2025July 2025: Focused effort on stabilizing and accelerating OpenNN on CPU and CUDA paths, improving maintainability, and preparing for GPU-accelerated training at scale. Delivered major features and quality improvements, fixed critical correctness bugs, and consolidated work across branches to ensure a cohesive codebase. The month yielded stronger reliability, faster iteration cycles, and better runtime performance for larger models.
July 2025
37 Commits • 10 Features
Jul 1, 2025July 2025: Focused effort on stabilizing and accelerating OpenNN on CPU and CUDA paths, improving maintainability, and preparing for GPU-accelerated training at scale. Delivered major features and quality improvements, fixed critical correctness bugs, and consolidated work across branches to ensure a cohesive codebase. The month yielded stronger reliability, faster iteration cycles, and better runtime performance for larger models.
June 2025
22 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary – Artelnics/opennn Key features delivered: - Codebase Merges and Stabilization: merged branches into mainline and stabilized the codebase to support smoother releases. Representative commits: 3bc96290be7360bec576102030265906b9de4a92; f34469aa132af769920f2b4a2dc1a17df460ff23. - Dense Network Enhancements and Fixes: Dense2D CUDA fix, dense cleanup, and dropout improvements to boost model reliability and throughput. Commits: 3fb1c22756aa4a39c92c81ddaae1c8e0552fa2c9; 979a674f8ddcfbc79079e51a0fda3181f377aa0d; 9827ced02eefd1839a6f7c2b8605f8b286bffde9. - CUDA Build, Tooling, and Testing Improvements: CMake-based CUDA and Linux CUDA tooling improvements; added CUDA testing/analysis instrumentation to validate GPU code paths. Commits: 518c8492def89bb965702c960c6699254d3968e8; a8c8f0ace3adb1fcd038ce77e0118397815ac7d6; c21bdbb75a12224723c7c892c7a2e7d0a1234912. - VGG16 Integration: integrated VGG16 support/usage to expand model capabilities. Commit: 9d9f2061d02ddbdb43a35f986a75c23af0fafc68. - Codebase Cleanup/Refactoring and TODOs: systematic cleanup and minor refactors to reduce technical debt. Commits: 298627764470864bfe2f808ae161f398b55d4da7; 7c111c8d804581ce261548838a310b71bf289b4b; 557247ad73367a8e938b8f00c1882997a48c7cc0; 4877785d4a7474a40cb662735fa61af57d533fca. Major bugs fixed: - Convolution Padding Fix: ensured padding mode is 'same' for consistent output sizes. Commit: 82308c3e2e48e1fe72d83efdaa552e3c04a252c2. - CUDA Link and Runtime Fixes: resolved CUDA link errors preventing builds/run issues. Commit: d2328b271b1d80d1e1101f511d05b297ab1fa4ac. - CUDA Blank State Initialization Bug: fixed blank CUDA initialization state affecting computations. Commit: 19206dcb8e484c0450404b5e6bb8e42ed8dbb1d3. - Dense Softmax Calculation Fix: corrected dense softmax behavior. Commit: 66012564a38967b0f75d61705568641dfcc1d327. - Merge Commit Integration: an integration merge to align branches for this batch. Commit: 0b8bcdff1c4212f432a3f703a2b2a4220b9681a0. Overall impact and accomplishments: - Stabilized mainline, enabling more reliable releases and faster iteration cycles. - Significantly improved GPU build reliability and validation of GPU paths, reducing runtime incidents. - Expanded model support (VGG16) and improved maintainability through disciplined cleanup and refactoring. - Demonstrated a strong blend of performance optimization, build engineering, and quality assurance. Technologies/skills demonstrated: - CUDA development and debugging, including optimization and parameter tuning. - Build tooling with CMake and Linux CUDA workflows. - GPU testing instrumentation and validation for GPU code paths. - Code cleanup, refactoring, and dependency maintenance for long-term maintainability.
22 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary – Artelnics/opennn Key features delivered: - Codebase Merges and Stabilization: merged branches into mainline and stabilized the codebase to support smoother releases. Representative commits: 3bc96290be7360bec576102030265906b9de4a92; f34469aa132af769920f2b4a2dc1a17df460ff23. - Dense Network Enhancements and Fixes: Dense2D CUDA fix, dense cleanup, and dropout improvements to boost model reliability and throughput. Commits: 3fb1c22756aa4a39c92c81ddaae1c8e0552fa2c9; 979a674f8ddcfbc79079e51a0fda3181f377aa0d; 9827ced02eefd1839a6f7c2b8605f8b286bffde9. - CUDA Build, Tooling, and Testing Improvements: CMake-based CUDA and Linux CUDA tooling improvements; added CUDA testing/analysis instrumentation to validate GPU code paths. Commits: 518c8492def89bb965702c960c6699254d3968e8; a8c8f0ace3adb1fcd038ce77e0118397815ac7d6; c21bdbb75a12224723c7c892c7a2e7d0a1234912. - VGG16 Integration: integrated VGG16 support/usage to expand model capabilities. Commit: 9d9f2061d02ddbdb43a35f986a75c23af0fafc68. - Codebase Cleanup/Refactoring and TODOs: systematic cleanup and minor refactors to reduce technical debt. Commits: 298627764470864bfe2f808ae161f398b55d4da7; 7c111c8d804581ce261548838a310b71bf289b4b; 557247ad73367a8e938b8f00c1882997a48c7cc0; 4877785d4a7474a40cb662735fa61af57d533fca. Major bugs fixed: - Convolution Padding Fix: ensured padding mode is 'same' for consistent output sizes. Commit: 82308c3e2e48e1fe72d83efdaa552e3c04a252c2. - CUDA Link and Runtime Fixes: resolved CUDA link errors preventing builds/run issues. Commit: d2328b271b1d80d1e1101f511d05b297ab1fa4ac. - CUDA Blank State Initialization Bug: fixed blank CUDA initialization state affecting computations. Commit: 19206dcb8e484c0450404b5e6bb8e42ed8dbb1d3. - Dense Softmax Calculation Fix: corrected dense softmax behavior. Commit: 66012564a38967b0f75d61705568641dfcc1d327. - Merge Commit Integration: an integration merge to align branches for this batch. Commit: 0b8bcdff1c4212f432a3f703a2b2a4220b9681a0. Overall impact and accomplishments: - Stabilized mainline, enabling more reliable releases and faster iteration cycles. - Significantly improved GPU build reliability and validation of GPU paths, reducing runtime incidents. - Expanded model support (VGG16) and improved maintainability through disciplined cleanup and refactoring. - Demonstrated a strong blend of performance optimization, build engineering, and quality assurance. Technologies/skills demonstrated: - CUDA development and debugging, including optimization and parameter tuning. - Build tooling with CMake and Linux CUDA workflows. - GPU testing instrumentation and validation for GPU code paths. - Code cleanup, refactoring, and dependency maintenance for long-term maintainability.
June 2025
May 2025
18 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Artelnics/opennn focused on stabilizing and accelerating CUDA-enabled training and improving code quality. Key backend enhancements, architecture updates for CUDA-accelerated image classification, and solid QA/maintenance work delivered business value through faster, more reliable GPU workflows and a cleaner, scalable codebase.
May 2025
18 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Artelnics/opennn focused on stabilizing and accelerating CUDA-enabled training and improving code quality. Key backend enhancements, architecture updates for CUDA-accelerated image classification, and solid QA/maintenance work delivered business value through faster, more reliable GPU workflows and a cleaner, scalable codebase.
April 2025
20 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated neural network training and MNIST image classification for OpenNN, focusing on performance, scalability, and maintainability. Implemented end-to-end CUDA-enabled workflows across the network stack (perceptron, pooling, convolution), training loops, error calculation, and data processing. Integrated CUDA across optimizers (Adam, SGD, etc.), with dataset integration and targeted refactoring to support CUDA-enabled workloads. Completed a broad set of CUDA fixes and cleanups to stabilize GPU paths and prepare for larger-scale experiments.
20 Commits • 1 Features
Apr 1, 2025April 2025: Delivered GPU-accelerated neural network training and MNIST image classification for OpenNN, focusing on performance, scalability, and maintainability. Implemented end-to-end CUDA-enabled workflows across the network stack (perceptron, pooling, convolution), training loops, error calculation, and data processing. Integrated CUDA across optimizers (Adam, SGD, etc.), with dataset integration and targeted refactoring to support CUDA-enabled workloads. Completed a broad set of CUDA fixes and cleanups to stabilize GPU paths and prepare for larger-scale experiments.
April 2025
March 2025
18 Commits • 7 Features
Mar 1, 2025March 2025 monthly summary for Artelnics/opennn: Delivered substantial neural network performance, robustness, and usability enhancements with a focus on business value and technical excellence. Key features delivered include CNN performance and architecture improvements with optimized backprop, tensor mapping, data paths, and improved Flatten layer handling; GPU acceleration through CUDA support for neural network operations; expanded training configuration to improve initialization, strategy configuration, and consistent time units across optimizers; data handling and persistence improvements including CSV/XML compatibility and better missing-value handling; and core/library enhancements with Eigen/vectorization and refined build configuration. Additionally, evaluation and analytics were strengthened with ROC curve analysis, KS statistics, and improved reporting for training results. Code quality and maintainability were advanced through codebase cleanup and deprecation removal. This work reduces training time and improves model reliability, enhances deployment readiness, and provides stronger data governance and observability for model evaluation and results reporting.
March 2025
18 Commits • 7 Features
Mar 1, 2025March 2025 monthly summary for Artelnics/opennn: Delivered substantial neural network performance, robustness, and usability enhancements with a focus on business value and technical excellence. Key features delivered include CNN performance and architecture improvements with optimized backprop, tensor mapping, data paths, and improved Flatten layer handling; GPU acceleration through CUDA support for neural network operations; expanded training configuration to improve initialization, strategy configuration, and consistent time units across optimizers; data handling and persistence improvements including CSV/XML compatibility and better missing-value handling; and core/library enhancements with Eigen/vectorization and refined build configuration. Additionally, evaluation and analytics were strengthened with ROC curve analysis, KS statistics, and improved reporting for training results. Code quality and maintainability were advanced through codebase cleanup and deprecation removal. This work reduces training time and improves model reliability, enhances deployment readiness, and provides stronger data governance and observability for model evaluation and results reporting.
February 2025
10 Commits • 1 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for Artelnics/opennn: Delivered dataset IO and demo enhancements and strengthened XML parsing robustness. Focused on data workflow improvements, test data updates, and example/demo refinements to increase reliability and reproducibility of experiments. Implemented dataset serialization/deserialization, CSV loading, and XML persistence, plus robust handling of binary/categorical variables and defaults during data initialization. The work reduces data-loading errors, accelerates experimentation, and enhances overall product quality.
10 Commits • 1 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for Artelnics/opennn: Delivered dataset IO and demo enhancements and strengthened XML parsing robustness. Focused on data workflow improvements, test data updates, and example/demo refinements to increase reliability and reproducibility of experiments. Implemented dataset serialization/deserialization, CSV loading, and XML persistence, plus robust handling of binary/categorical variables and defaults during data initialization. The work reduces data-loading errors, accelerates experimentation, and enhances overall product quality.
February 2025
January 2025
9 Commits • 2 Features
Jan 1, 2025January 2025 (Month: 2025-01) — Artelnics/opennn delivered focused improvements to the training pipeline, data IO, and dataset management, delivering measurable business value through more robust workflows, faster iteration, and improved maintainability. Key features delivered include enhancements to image classification training (padding and convolution config, dataset setup tweaks, and training workflow refinements with debugging aids and Quasi-Newton optimization enablement) and data loading/dataset management improvements (refactored XML parsing, robust data loading/saving, and dataset handling for examples with cleanup of files and config paths). A critical bug fix addressed confusion matrix calculations by correcting dimensions and batch input initialization, ensuring reliable evaluation metrics. Overall, these changes reduce data pipeline risk, shorten model iteration cycles, and improve data quality and reproducibility. Technologies and skills demonstrated include Python-based ML workflow tuning, XML parsing and dataset management, robust IO patterns, convolution padding handling, and optimization strategy refinements.
January 2025
9 Commits • 2 Features
Jan 1, 2025January 2025 (Month: 2025-01) — Artelnics/opennn delivered focused improvements to the training pipeline, data IO, and dataset management, delivering measurable business value through more robust workflows, faster iteration, and improved maintainability. Key features delivered include enhancements to image classification training (padding and convolution config, dataset setup tweaks, and training workflow refinements with debugging aids and Quasi-Newton optimization enablement) and data loading/dataset management improvements (refactored XML parsing, robust data loading/saving, and dataset handling for examples with cleanup of files and config paths). A critical bug fix addressed confusion matrix calculations by correcting dimensions and batch input initialization, ensuring reliable evaluation metrics. Overall, these changes reduce data pipeline risk, shorten model iteration cycles, and improve data quality and reproducibility. Technologies and skills demonstrated include Python-based ML workflow tuning, XML parsing and dataset management, robust IO patterns, convolution padding handling, and optimization strategy refinements.
December 2024
17 Commits • 3 Features
Dec 1, 2024Dec 2024 monthly summary for Artelnics/opennn highlighting key features delivered, major bug fixes, and business impact. Focused on enabling CNN architectures, performance optimizations, CI/CD improvements, and code hygiene to accelerate research and product readiness.
17 Commits • 3 Features
Dec 1, 2024Dec 2024 monthly summary for Artelnics/opennn highlighting key features delivered, major bug fixes, and business impact. Focused on enabling CNN architectures, performance optimizations, CI/CD improvements, and code hygiene to accelerate research and product readiness.
December 2024
November 2024
24 Commits • 8 Features
Nov 1, 2024November 2024 — Artelnics/opennn monthly performance overview Key features delivered: - Training Strategy XML Configuration and Pooling Enhancements: Added pooling support and XML-based training strategy configuration improvements to enable more flexible and scalable model training workflows. - Dataset Initialization and Setup: Established a robust dataset configuration and setup process to streamline new experiment injections and reproducibility. - Image Output Enhancements: Improved image output handling and formatting for clearer visualization and easier downstream processing. - Codification Enhancements: Introduced codification improvements to strengthen modeling pipelines and reproducibility. Code quality and maintenance: - Codebase Cleanup and Maintenance: Performed extensive cleanup and minor maintenance across the batch to improve readability and maintainability. - Code Cleanup and Refactoring: Additional cleanup/refactoring efforts to reduce technical debt and improve future extensibility. - Branch Integration: Merged changes from the feature branch into the batch (merge 209d659d) to maintain a cohesive mainline. Test and reliability improvements: - Test Infrastructure Enhancements: Strengthened test suite and instrumentation, including pch/test fixes, test additions, and expanded coverage. - Dataset Handling Bug Fix: Resolved dataset loading/handling issue introduced in this batch. - Compile Fix: Addressed a compilation error introduced by recent changes to restore build stability. Overall impact and accomplishments: - Improved training configurability and efficiency via XML-driven strategies and pooling; reduced time-to-train and increased experimentation throughput. - More robust dataset initialization and handling, lowering setup risk and improving reproducibility across runs. - Elevated code quality, maintainability, and build stability through systematic cleanup, refactoring, and merge discipline. - Enhanced test reliability and coverage, accelerating feedback loops and release readiness. Technologies/skills demonstrated: - XML-based configuration, pooling integration, dataset orchestration, and data pipeline robustness. - Build stability, compile-time fixes, and refactoring for long-term maintainability. - Test infrastructure design, instrumentation, and coverage expansion.
November 2024
24 Commits • 8 Features
Nov 1, 2024November 2024 — Artelnics/opennn monthly performance overview Key features delivered: - Training Strategy XML Configuration and Pooling Enhancements: Added pooling support and XML-based training strategy configuration improvements to enable more flexible and scalable model training workflows. - Dataset Initialization and Setup: Established a robust dataset configuration and setup process to streamline new experiment injections and reproducibility. - Image Output Enhancements: Improved image output handling and formatting for clearer visualization and easier downstream processing. - Codification Enhancements: Introduced codification improvements to strengthen modeling pipelines and reproducibility. Code quality and maintenance: - Codebase Cleanup and Maintenance: Performed extensive cleanup and minor maintenance across the batch to improve readability and maintainability. - Code Cleanup and Refactoring: Additional cleanup/refactoring efforts to reduce technical debt and improve future extensibility. - Branch Integration: Merged changes from the feature branch into the batch (merge 209d659d) to maintain a cohesive mainline. Test and reliability improvements: - Test Infrastructure Enhancements: Strengthened test suite and instrumentation, including pch/test fixes, test additions, and expanded coverage. - Dataset Handling Bug Fix: Resolved dataset loading/handling issue introduced in this batch. - Compile Fix: Addressed a compilation error introduced by recent changes to restore build stability. Overall impact and accomplishments: - Improved training configurability and efficiency via XML-driven strategies and pooling; reduced time-to-train and increased experimentation throughput. - More robust dataset initialization and handling, lowering setup risk and improving reproducibility across runs. - Elevated code quality, maintainability, and build stability through systematic cleanup, refactoring, and merge discipline. - Enhanced test reliability and coverage, accelerating feedback loops and release readiness. Technologies/skills demonstrated: - XML-based configuration, pooling integration, dataset orchestration, and data pipeline robustness. - Build stability, compile-time fixes, and refactoring for long-term maintainability. - Test infrastructure design, instrumentation, and coverage expansion.
October 2024
9 Commits • 2 Features
Oct 1, 2024Summary for 2024-10: Delivered key stability and data pipeline enhancements in Artelnics/opennn, focusing on memory-safe BackPropagation, robust data loading, and corrected network configuration through a set of targeted fixes and feature improvements. These changes streamline the MNIST experimentation workflow, improve data persistence, and reduce configuration issues across neural network layers.
9 Commits • 2 Features
Oct 1, 2024Summary for 2024-10: Delivered key stability and data pipeline enhancements in Artelnics/opennn, focusing on memory-safe BackPropagation, robust data loading, and corrected network configuration through a set of targeted fixes and feature improvements. These changes streamline the MNIST experimentation workflow, improve data persistence, and reduce configuration issues across neural network layers.
October 2024
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Quality Metrics
Correctness82.4%
Maintainability82.2%
Architecture78.2%
Performance74.8%
AI Usage20.8%
Skills & Technologies
Programming Languages
CC++CMakeCMakeScriptCSVCUDAGoogle TestJavaScriptPropertiesPython
Technical Skills
AI DevelopmentAlgorithm DesignAlgorithm ImplementationAlgorithm TestingBatch NormalizationBuild AutomationBuild ConfigurationBuild ProcessBuild SystemBuild System ConfigurationBuild System ManagementBuild SystemsC++C++ DevelopmentC++ Programming
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