Get Started with TinyML
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
Make your IoT device feel, hear and see things with TinyML
Many IoT devices are very simple: just a radio sending raw sensor values to the cloud. But this limits the usefulness of a deployment. A sensor can send that it saw movement in front of it, but not what it saw. Or a sensor might notice that it's being moved around, but not whether it's attached to a vehicle or is just being carried around. The reason is simple: for knowing what happens in the real world you'll need lots of data, and sending all that data over your IoT network quickly drains your battery and racks up your network bill.
How can we do better? In this talk we'll look at ways to draw conclusions from raw sensor data right on the device. From signal processing to running neural networks on the edge. It's time to add some brains to your IoT deployment. In this talk you'll learn:
- What is TinyML, and how can your sensors benefit from it?
- How signal processing can help you make your TinyML deployment more predictable, and better performing.
- How you can start making your devices feel, hear and see things - all running in realtime on Cortex-M-class devices.
- Hands-on demonstrations: from initial data capture from real devices, to building and verifying TinyML models, and to deployment on device