The Greatest Guide To ML for embedded systems
The Greatest Guide To ML for embedded systems
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In this particular module, we will look at how neural networks get the job done, the way to train them, and the way to use them to carry out inference within an embedded system. We are going to keep on the previous demo of creating a movement classification system using movement AI-enhanced embedded design data gathered from a smartphone or Arduino board.
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Improved performance: AI algorithms can optimize procedures and lower waste, resulting in far more efficient source utilization and cost savings
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