======= Support ======= .. image:: https://camo.githubusercontent.com/11b2f47d7b4af17ef3a803f57c37de3ac82ac039/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70617970616c2d646f6e6174652d79656c6c6f772e737667 :target: https://www.paypal.me/jeff1evesque .. image:: https://camo.githubusercontent.com/c705adb6695b3d8f60b9a005674cb58b3f1ef1cc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f6e6174652d626974636f696e2d677265656e2e737667 :target: http://coinbase.com/jeff1evesque Donations are very appreciated. Smaller donations, could fund a latté, during a late night meddling code. While larger donations, could fund further research, by assisting the cost for the following: - server(s): this could be made open to the public, and implementing machine-learning. - peripheral device(s): these device(s) could connect to the machine-learning server(s): - `raspberry pi `_: these devices could communicate to the machine-learning server(s), or *peripheral device(s)*. - `xbee chip `_: these chips could implement the `zigbee `_ wireless protocol, allowing peripheral device(s) to transmit data between one another, and finally to the machine-learning server(s). - `sensor `_: multiple types of sensors could be connected via the `zigbee `_ wireless protocol to other sensor(s), raspberry pi(s), or directly to the machine-learning server(s).