AI engineering glossary · Deep learning core
What is cross-entropy loss?
Cross-entropy loss (also called Log loss, Categorical cross-entropy): A loss for classification that measures the gap between predicted probabilities and the true labels: for one example it is minus the log of the probability assigned to the correct class. Confident wrong answers are punished heavily.
Cross-entropy loss: an example
If the model gives the true class probability 0.9 the loss is -ln 0.9, about 0.105; at 0.1 it is about 2.30.
Related terms
More Deep learning core terms
- Curriculum learning
- Cyclical learning rate
- Data parallelism
- Deep learning
- Deep neural network
- DropConnect
- Dropout
- Dying ReLU
Where you learn it
Cross-entropy loss is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.