Deep Learning in Simple Words

There are two main steps in the conventional machine learning or ML pipeline: feature extraction and classification. The goal of feature extraction is to represent data in a numerical space, also called feature space. The goal of classification is to determine the group that each data point belongs to. If we can simply design a classifier to separate data into classes within the feature space, it means that feature extraction and classification work as needed. However, the story is not always as simple as this.