Machine Learning, Supervised Learning, Unsupervised Learning, Artificial Intelligence, Data Science.
and the learning process are entirely supervised but we don’t require the step of manually labeling training data. Example 1: Autoencoders A classic example for this are Autoencoders where input and output data are entirely the same. The purpose of their architecture is to first compress the input