cross-entropy

Cross-entropy is commonly used to quantify the difference between two probability distributions. In the context of machine learning, it is a measure of error for categorical multi-class classification problems. Usually the “true” distribution (the one that your machine learning algorithm is trying to match) is expressed in terms of a one-hot distribution. For example, suppose … Read more