Training loss: Difference between revisions

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 #[[Mean squared error]] (MSE) Loss: This loss function is the most commonly employed for [[regression]] problems. It measures the average squared difference between predicted output and actual output.
-#[[Binary cross-entropy]] (BCE): Used in [[binary classification]] problems where the objective is to accurately predict one of two possible [[class]]es, this statistic measures the difference between the predicted probability of a [[positive class]] and actual [[binary label]].
+#[[Binary cross-entropy]] (BCE): Used in [[binary classification]] problems where the objective is to accurately predict one of two possible [[class]]es, this statistic measures the distance between the predicted probabilities and the actual target values.
-#[[Categorical cross-entropy]] (CCE): Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label.
+#[[Categorical cross-entropy]] (CCE): Used in [[multiclass classification]] problems to predict one of several classes, this statistic measures the difference between a [[predicted probability distribution]] and an actual [[one-hot encoded]] class [[label]].
-#Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes.
+#[[Softmax cross-entropy]]: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the [[categorical cross-entropy]] loss for each class and then takes its average across all classes.
-#KL-Divergence: This statistic measures the difference in probability distributions. It's commonly employed when training generative models such as Generative Adversarial Networks (GANs).
+#[[KL-divergence]]: This statistic measures the difference in probability distributions. It's commonly employed when training [[generative models]] such as [[Generative Adversarial Network]]s (GANs).
 ==How Training Loss is Used==