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Add BinaryOperation::Min and Max, and UnaryOperation::Log10, min and max where a Unary min/max returns the minium or maximum value of the input vector as the same shaped output vector.  Also removed broken javascript swig build and with these fixes I'm able to now create a proper `power_to_db` featurizer (see dgml below), and this featurizer improves my audio model accuracies from 80% to 98% on the premonition mosquito classification models.  This new featurizer is implementing the following algorithm from `librosa`:

def power_to_db(S, ref=1.0, amin=1e-5, dbmax=80):
    log_spec = 10.0 * np.log10(np.maximum(amin, S))
    log_spec -= 10.0 * np.log10(np.maximum(amin, ref))
    return np.maximum(log_spec, log_spec.max() - dbmax)


Related work items: #2909

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Embedded Learning Library

The Embedded Learning Library (ELL) allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit. The deployed models run locally, without requiring a network connection and without relying on servers in the cloud. ELL is an early preview of the embedded AI and machine learning technologies developed at Microsoft Research.

Go to our website for tutorials, instructions, and a gallery of pretrained ELL models for use in your projects.

ELL is a work in progress, and we expect it to change rapidly, including breaking API changes. Despite this code churn, we welcome you to try it and give us feedback.


See LICENSE.txt.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information on this code of conduct, see the Code of Conduct FAQ or contact with any additional questions or comments.

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Technical Documentation