# cross-entropy

## What is cross-entropy?

In the artificial intelligence industry, cross-entropy is often used to train classification models by quantifying how well the predicted probabilities align with the actual class labels. Specifically, it calculates the negative log likelihood of the true labels given the predicted probabilities. This helps in adjusting the model’s parameters during the training process to minimize the error and improve accuracy. Cross-entropy is particularly effective in models dealing with multi-class classification problems, such as neural networks used in image recognition or natural language processing. By penalizing incorrect predictions more heavily, it ensures that the model becomes more confident in its predictions over time, leading to better performance on unseen data.

Cross-entropy is a loss function used in artificial intelligence to measure the difference between two probability distributions, typically the predicted output and the actual output.

### Examples

- Image Recognition: In a model designed to recognize handwritten digits, cross-entropy can be used to compare the predicted probability distribution over the 10 possible digit classes (0-9) with the actual class label. If the model predicts a high probability for the correct digit, the cross-entropy loss will be low.
- Natural Language Processing: When training a language model to predict the next word in a sentence, cross-entropy loss can measure how well the predicted probability distribution over the vocabulary matches the actual next word. A lower cross-entropy loss indicates better performance in generating coherent text.

### Additional Information

- Cross-entropy loss is also known as log loss.
- It is particularly useful for classification tasks where the output is a probability distribution over multiple classes.