An Introduction To Supervised Deep Learning For Non-Techies

Supervised Deep Learning is similar to concept learning in humans and animals, the difference being that the student in the former case is a computational network. Supervised deep learning frameworks are trained using well-labelled data. It teaches the learning algorithm to generalise from the training data and to implement in unseen situations. After completing the training process, the model is tested on a subset of the testing set to predict the output. Thus, datasets containing inputs and correct outputs become critical as they help the model learn faster.  Regression and classification are two subfields of supervised machine learning. Regression always predicts a continuous value using the training dataset to determine it. These outputs have a probabilistic interpretatio