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1
Introduction
2
Overview
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Conceptualization
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Data Visualization
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Data Interpretation
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Data Management
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Data Properties
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Preprocessing
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Model Wizard
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Training Models
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Model Evaluation
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Real World Example
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Looking at the Models
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Back to the Slides
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Summary
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Slow Dynamics
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Can ML Model Handle Noise
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Do you have support for recurrent
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Can you elaborate on segmentation
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Is imagimob Studio onpremise or cloud
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Conclusion
Description:
Dive into a comprehensive tutorial on building production-ready models using Imagimob AI for tinyML applications. Explore the Imagimob AI development platform, designed to streamline the creation of Artificial Neural Network (ANN) models for time-series data on resource-constrained devices. Learn about the platform's low-code approach while gaining access to powerful tools for sensor connection, data visualization, preprocessing, and model integration. Follow along as the tutorial covers key concepts including data interpretation, management, and properties, as well as model training, evaluation, and real-world examples. Gain insights into handling slow dynamics, noise in ML models, recurrent networks, and segmentation techniques. Discover whether Imagimob Studio is available on-premise or in the cloud, and understand how this platform can accelerate your tinyML development process.

TinyML AutoML Tutorial: Building Production-ready Models with Imagimob AI

tinyML
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