Difference between revisions of "Cross-Entropy Loss"
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| − | [ | + | [https://www.youtube.com/results?search_query=Cross+Entropy+Loss+deep+learning+hyperparameter YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search] |
* [[Loss]] | * [[Loss]] | ||
| − | * [[Algorithm Administration#Hyperparameter|Hyperparameter]] | + | * [[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]] |
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| − | 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. [ | + | 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. [ | + | 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. [https://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu] |
| − | + | https://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png | |
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
