https://github.com/ml5js/Intro-ML-Arts-IMA-F24/tree/main/02-transfer-learning
"In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.”
Supervised Learning is a strategy that involves a "teacher" that trains the learning system. For example, consider facial recognition. The "teacher" shows the network a bunch of faces (the teacher already knows the names associated with each face). The learning system makes its guesses and the teacher provides the answers. The learning system can then compare its answers to the known “correct” ones and make adjustments according to its errors.
https://github.com/EliSchwartz/imagenet-sample-images/blob/master/gallery.md
Transfer Learning: using knowledge gained from solving one problem, and applying it to a different but related problem
https://teachablemachine.withgoogle.com/
Lets you train classifiers for images, sound, audio
Already pretrained with ImageNet, the user uploads their customized classers
Output from last layer of MobileNet:
