TinyML is opening up incredible new applications for sensors on embedded devices, from predictive maintenance to health applications using vibration, audio, biosignals and much more! 99% of sensor data is discarded today due to power, cost or bandwidth constraints
This webinar introduces why ML is useful to unleash meaningful information from that data, how this works in practice from signal processing to neural networks, and walks the audience through hands-on examples of gesture and audio recognition using Edge Impulse.
What you will learn:
- What is TinyML and why does it matter for real-time sensors on the edge
- Understanding of the applications and types of sensors that benefit from ML
- What kinds of problems ML can solve and the role of signal processing
- Hands-on demonstration of the entire process: sensor data capture, feature extraction, model training, testing and deployment to any device
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