Technext Jpn
Computer Scientist
Tokyo, Japan
Summary
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RockstarTechnext Jpn is a Tokyo-based computer scientist with a Ph.D. in Computer Science and 10 years of hands-on experience building and evaluating machine learning systems. He specializes in PyTorch model analysis and has contributed significant functionality to the open-source torchstat project, adding FLOPs, memory usage computations, and improved reporting that sharpen model performance insights. Comfortable bridging research and engineering, he focuses on practical tooling that helps teams understand and optimize neural architectures. Known for refactoring and making analysis codebases more maintainable, he brings rigorous academic training to production-focused ML engineering.
10 years of coding experience
Github Skills (5)
memory-profiling10
pytorch10
machine-learning10
python10
modeling10
Programming languages (1)
Github contributions (5)
Swall0w/torchstat
Oct 2018 - Nov 2018
Model analyzer in PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 35 commits, 2 PRs in 7 days
Contributions summary:Technext primarily focused on adding and updating functionality for model analysis within a PyTorch framework. They implemented code to compute various metrics, including FLOPs and memory usage, for different PyTorch modules like convolutional layers and ReLU activations. The user also refactored the project structure by renaming modules and enhancing the reporting features to include the computed statistics. These changes suggest a focus on improving model evaluation and understanding model performance.
pytorchpythondeep-learningmachine-learninganalyzer
Swall0w/BBox-Label-Tool
Jun 2017 - May 2018
Contributions:31 commits, 8 PRs, 24 pushes in 11 months
pytorchdeep-learningboundingobject-detectioncomputer-vision
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