With the loss function, neural network training is reduced to the process of optimally selecting the coefficients of the matrix of weights in order to minimize the error. This function should correspond to the task, for example, categorical cross-entropy for the classification problem or the square of the difference for regression. Differentiability is also an essential property of the loss function if the backpropagation method is used to train the network. Let's look at some of the popular loss functions that are used in neural networks:
- The mean squared error (MSE) loss function is widely used for regression and classification tasks. Classifiers can predict continuous scores, which are intermediate results that are only ...
