Eclectic-AI

There are many styles of training machine learning models including the familiar supervised and unsupervised learning to active learning, semi-supervised learning and self-supervised learning. In this post, I will explain the difference between semi-supervised and self-supervised styles of learning. To get started, let us first recap what is  supervised learning, the most popular machine learning methodology to build predictive models. Supervised learning uses annotated or labeled data to train predictive models. A   label   attached to a data vector is nothing but the response that the predictive model should generate  for that data vector as input during the model training. For example, we will label pictures of cats and dogs with labels   cat   and   dog  to train  a Cat versus Dog classifier. We assume a large enough training data set with labels is available w hen building a classifier. When there are no labels attached to the training data, then the learning style is known as uns