Making a practical machine learning project, end-to-end #MLmonday #ML #MachineLearning #Gaming
Jeremy’s blog posts the process of developing and implementing a practical machine learning (ML) project.
The goal of this project was to make a system that could play the card game Quiddler alongside human players. The reason for doing it was to have a vehicle to learn new technology. Therefore, to make this challenging I wanted a cloud hosted system to read the physical cards from a camera and deduce the best play.
When you visit this web page you can open the webcam and then take a photo of the cards in your hand and the card on the deck. Provide the number of cards in your hand as a hint and then ask for the best play. The screenshot shows that the cards have been recognised successfully and the suggested play is to pick up the deck card “o” and drop the “a”, then make the words “xenon” and “udo” for a score of 37.
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.