liquidinstruments/moku-examples
Oct 2024 – Jan 2025
2 Months active
Languages Used
PythonJSON
Technical Skills
AutoencodersDeep LearningMachine LearningMatplotlibNumPyTensorFlow
Maxwell Ashurst
PROFILE
Maxwell Ashurst
Maxwell enhanced the liquidinstruments/moku-examples repository by updating the Autoencoder example to train on random walk time-series data, requiring modifications to both the data generation pipeline and the model architecture. Using Python, TensorFlow, and NumPy, he enabled the autoencoder to generalize across variable-length patterns, broadening its applicability for demonstrations and experiments. In a subsequent project, Maxwell delivered a neural network model package for the Moku device, adding configuration files and documentation to support customer training and deployment of trained networks. His work demonstrated depth in deep learning and embedded systems, focusing on practical integration and extensibility for end users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
2,008
Activity Months2
Your Network
23 peopleSame Organization
@liquidinstruments.com10
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered Neural Network Model Package for the Moku device in liquidinstruments/moku-examples. Added model configuration files (.linn) and README to enable customers to upload trained networks and participate in customer training sessions. This aligns with product adaptability, faster onboarding, and expanded professional services capabilities.
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered Neural Network Model Package for the Moku device in liquidinstruments/moku-examples. Added model configuration files (.linn) and README to enable customers to upload trained networks and participate in customer training sessions. This aligns with product adaptability, faster onboarding, and expanded professional services capabilities.
January 2025
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10: Focused on updating the Autoencoder example in liquidinstruments/moku-examples to train on random walks instead of ringdown signals. This change required updating the data generation pipeline and adapting the model architecture to support time-series learning, enabling the autoencoder to learn and reconstruct variable-length random walk patterns. The work improved the example's ability to demonstrate generalization to diverse time-series data, broadening its applicability for demonstrations and experiments.
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10: Focused on updating the Autoencoder example in liquidinstruments/moku-examples to train on random walks instead of ringdown signals. This change required updating the data generation pipeline and adapting the model architecture to support time-series learning, enabling the autoencoder to learn and reconstruct variable-length random walk patterns. The work improved the example's ability to demonstrate generalization to diverse time-series data, broadening its applicability for demonstrations and experiments.
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AutoencodersDeep LearningEmbedded SystemsMachine LearningMatplotlibNumPyTensorFlow
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