neuralnetwork

Buy Me a Coffee☕ *My post explains optimizers in PyTorch. A loss function is the function which can get the mean(average) of the sum of the losses(differences) between a model's predictions and true values(train or test data) to optimize a model during training or to evaluate how good a model is during testing. *Loss function is also called Cost Function or Error Function. There are popular loss functions as shown below: (1) L1 Loss: can compute the mean(average) of the sum of the absolute losses(differences) between a model's predictions and true values(train and test data). 's formula: is used for…

A loss function is the function which can get the difference(gap) between a model's predictions and true values to evaluate how good a model is. *Loss function is also called Cost Function or Error Function. There are popular loss function as shown below: (1) L1 Loss: can compute the average of the sum of the absolute differences between a model's predictions and true values. 's formula is as shown below: is also called Mean Absolute Error(MAE). is L1Loss() in PyTorch. (2) L2 Loss: can compute the average of the sum of the squared differences between a model's predictions and true…