Hallo alle zusammen. Ich freue mich sehr euch zu einem weiteren Knowledge Exchange Group Workshop einladen zu dürfen. Machine Learning mit Processing und Wekinator. Alle weiteren Infos findet ihr im folgenden Workspace:
The so-called ‘smart home’ often comes across looking incredibly dumb. Either you have to shell out lots of money to replace perfectly functional appliances for their Internet-connected equivalents — which might then be vulnerable to hacking or whose functionality could be bricked at manufacturer whim. (…)
What they’ve built so far does not offer as many remote control options as a fully fledged, IoT-enabled appliance scenario could. But if it’s mostly signals intelligence on what’s going on indoors that you want — plus the ability to leverage that accrued real-time intel to support contextually aware apps for the lived environment — their approach looks very promising. (…)
The system involves using a single custom plug-in sensor board that’s packed with multiple individual sensors — but, crucially from a privacy point of view, no camera. The custom sensor (shown in the diagram below) uses machine learning algorithms to process the data it’s picking up, so it can be trained to identify various types of domestic activity, such as (non-smart) appliances being turned on — like a faucet, cooker or blender. It can even identify things like cupboard doors or a microwave door being opened and closed; know which burner on your hob is on; and identify that a toilet has been flushed.
d4 is an experiment in using React to produce data-driven documents (ala d3) that are performant and understandable. This is not a library, but rather a demonstration that it's possible (and preferable) to use React instead of the core of d3.
Weitere Demos: https://github.com/joelburget/d4/tree/master/demo