Difference between revisions of "Cross-Entropy Loss"
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[https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search] | [https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search] | ||
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* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] | * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] | ||
Latest revision as of 19:58, 2 September 2023
YouTube search... ...Google search
- Loss
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. Neural Architecture Search for Deep Face Recognition | Ning Zhu
