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on
1
Intro
2
Summary
3
Platform Overview
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Prediction Tab
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Model Data Relevance Indicator
6
Customization
7
Compact models
8
Sensors
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MCUs
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Floats
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QA
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Roadmap
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Cloud dependency
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Time to iteration
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Big data
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Time taken for iteration
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Ideal edge hardware device
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Ideal collaborators
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Closing remarks
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Did you try to implement the generated model
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Top questions
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Reconfigurable spiking neural network
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Conclusion
Description:
Explore the challenges and solutions in creating compact machine learning models for edge devices in this tinyML EMEA 2021 Partner Session. Dive into Neuton.ai's approach to balancing model size and accuracy, evaluating model quality, and ensuring explainability in neural networks. Learn how to assess training data, interpret model decisions, and identify key parameters for building efficient tiny models. Discover techniques for monitoring model performance, detecting decay, and evaluating prediction credibility. Gain insights into customization, hardware considerations, and the future of tinyML implementation through this comprehensive presentation by Blair Newman, CTO of Neuton.ai.

Avoiding Loss of Quality in Tiny Models - Neuton.ai Partner Session

tinyML
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