Disclosure: Please note that the content in this blog was written with the assistance of OpenAI's ChatGPT language model.
Categorical cross-entropy is a commonly used loss function in machine learning, particularly in classification tasks where the output variable is a categorical variable with two or more classes. It measures the difference between the true probability distribution of the classes and the predicted probability distribution.
Mathematically, the categorical cross-entropy loss is calculated as follows:
Loss = - ∑(y * log(y_hat))
y is a one-hot encoded vector representing the true class labels,
y_hat is the predicted probability distribution over the classes, and the sum is taken over all classes.
In a simple terms, if you remove all the jargons,
Categorical cross-entropy is a mathematical formula that is used in machine learning to help a computer learn how to classify things into different categories, like sorting pictures of animals into categories like "dog", "cat", or "bird".
The formula looks at how well the computer's guesses match the correct answers, and it tries to adjust the computer's guesses so that they become better over time.
The goal of the formula is to help the computer learn how to make better predictions by minimizing the difference between the computer's guesses and the correct answers. This formula is often used in deep learning models, which are a type of machine learning model that is based on neural networks.