Hands-On Transfer Learning with Python

Supervised learning

Supervised learning algorithms are a class of algorithms that utilize data samples (also called training samples) and corresponding outputs (or labels) to infer a mapping function between the two. The inferred mapping function or the learned function is the output of this training process. The learned function is then utilized to correctly map new and unseen data points (input elements) to test the performance of the learned function.

Some key concepts for supervised learning algorithms are as follows:

• Training dataset: The training samples and corresponding outputs utilized during the training process are termed as training data. Formally, a training dataset is a two-element tuple consisting of an input element (usually ...