liquidinstruments/moku-examples
Nov 2025 – Dec 2025
2 Months active
Languages Used
Python
Technical Skills
JupyterJupyter Notebookdata analysisdata visualizationmachine learningneural networks
Roger
PROFILE
Roger
Roger contributed to the liquidinstruments/moku-examples repository by developing and refining Jupyter Notebooks focused on neural network-based anomaly detection for noisy waveform data. He enhanced the neural network notebook with CUDA driver diagnostics and TensorFlow optimizations, improving both usability and performance transparency. Roger introduced a dedicated anomaly detection notebook, implementing real-time model training and artificial anomaly testing utilities to strengthen validation and quality assurance. His work included reorganizing configuration files for maintainability and updating visualization workflows for clarity. Leveraging Python, data analysis, and machine learning, Roger delivered features that streamlined experimentation and improved the reliability of data insights for Moku devices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
3,515
Activity Months2
Your Network
27 peopleSame Organization
@liquidinstruments.com12
Shared Repositories
15
Ben NizetteMember
Brian NeffMember
Dylan PerezMember
EC2 Default UserMember
EC2 Default UserMember
Heyang LongMember
Indira Shinn ReesMember
jasonliquidinstMember
jobinMember
Work History
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for liquidinstruments/moku-examples focusing on the Enhanced Anomaly Detection in Noisy Waveforms feature and associated tooling improvements. Work concentrated on reliability, maintainability, and demonstrable business value through improved data quality insights and streamlined experimentation workflows.
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for liquidinstruments/moku-examples focusing on the Enhanced Anomaly Detection in Noisy Waveforms feature and associated tooling improvements. Work concentrated on reliability, maintainability, and demonstrable business value through improved data quality insights and streamlined experimentation workflows.
December 2025
November 2025
2 Commits • 2 Features
Nov 1, 2025Month 2025-11: Focused on enhancing neural network notebooks in the liquidinstruments/moku-examples repo, delivering usability improvements, diagnostics, and anomaly-detection workflows to accelerate experimentation and real-time analysis for Moku devices.
November 2025
2 Commits • 2 Features
Nov 1, 2025Month 2025-11: Focused on enhancing neural network notebooks in the liquidinstruments/moku-examples repo, delivering usability improvements, diagnostics, and anomaly-detection workflows to accelerate experimentation and real-time analysis for Moku devices.
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture80.0%
Performance80.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
JupyterJupyter NotebookPythondata analysisdata visualizationmachine learningneural networkssignal processing
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