Multi-class SVM Loss - PyImageSearch

A couple weeks ago,we discussed the concepts of both linear classification and parameterized learning. This type of learning allows us to take a set of input data and class labels, and actually learn a function that maps the input to the output predictions, simply by defining a set of parameters and optimizing over them. Our linear classification tutorial focused mainly on the concept of a scoring function and how it can be used to map the input data to class labels. But in order to actually "learn" the mapping from the input data to class labels, we need to discuss two important concepts: In today and next week's blog posts, we'll be discussing two loss functions that you'll commonly see when applying Machine Learning, Neural Networks, and Deep Learning algorithms: To learn more about your first loss function, Multi-class SVM loss, just keep reading. At the most basic level, a loss function is simply used to quantify how "good" or "bad" a given predictor is at classifying the input data points in a dataset.