Teachable Machine Learning is an approach to machine learning that allows users without programming skills to train their own models – usually through a user-friendly interface like Google's Teachable Machine. For example, users can upload images, sounds, or poses to create an AI model that recognizes and classifies them. In short: Teachable Machine Learning makes it easy for anyone to create AI models by teaching the machine to recognize patterns from examples.
IMAGE DETECTOR / IMAGE MODEL
Calculator:
-Teachable Machine trained on digits
-Percentage indication of probability
-Program recognizes the digits held in front of the camera
-Function: Should be able to calculate using the operators
Not me:
-Pictures of me and pictures only of the background
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Percentage indication of probability
Mugshot:
-Use pictures of fellow students to build an AI model
-Pictures in front of a neutral background -> avoid disturbances
-Percentage indication of probability
-From a certain probability a gimmick
AUDIO DETECTOR / AUDIO MODEL
Music:
- Record start and stop from fellow students
- Different background noises to train the AI better on the words
- When start: song begins
- When stop: song stops
MOTION DETECTOR / MOTION MODEL
Start_Stop:
-Hands up: start
-Hands down: stop
-No function considered so far
PROBLEMS
- Teachable Machine Learning very inaccurate
- Many factors influence the accuracy
- Nice layout for the functions
- Uncertain which idea I want to pursue
ADITIONAL INFORMATION
This is just a short summary – check out my website to learn more about my projects and experiences!