liquidinstruments/moku-examples
Oct 2024 – Jan 2025
2 Months active
Languages Used
PythonJSON
Technical Skills
AutoencodersDeep LearningMachine LearningMatplotlibNumPyTensorFlow
Maxwell Ashurst
PROFILE
Maxwell Ashurst
Over a two-month period, contributed to the liquidinstruments/moku-examples repository by developing and enhancing machine learning features for embedded systems. Updated the Autoencoder example to train on random walk time-series data, which involved modifying the data generation pipeline and adapting the model architecture for variable-length inputs using Python, TensorFlow, and NumPy. Later, delivered a neural network model package for the Moku device, including configuration files and documentation to support customer training and deployment of trained networks. The work focused on practical integration of deep learning models into embedded workflows, emphasizing adaptability and ease of use for both demonstrations and customer onboarding.
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
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
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.
Activity
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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
Repositories Contributed To
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