What You Need to Know about Sparse Categorical Cross Entropy
Sparse Categorical Cross Entropy is a loss function that is commonly used in machine learning algorithms to train classification models. It is an extension of the Cross Entropy loss function that is used for binary classification problems. The sparse categorical cross-entropy is used in cases where the output labels are represented in a sparse matrix format.
In this story, I will discuss the concept of sparse categorical cross-entropy in detail. I will start by discussing the basics of loss functions and cross-entropy, then move on to explain what sparse categorical cross-entropy is and how it works. I will also discuss the advantages and disadvantages of using sparse categorical cross-entropy and some use cases where it is commonly used.
Loss Functions
In machine learning, a "loss function" is a function that measures the difference between the predicted output and the actual output of a model. The loss function is used to train the model by adjusting the weights of the parameters in the model to minimize the loss. The aim of the loss function is to find the optimal set of weights that can predict the output accurately.
There are many different types of loss functions that can be used for different types of problems. The choice of loss function depends on…
