Cross-Entropy Loss
A loss function for classification that measures the difference between predicted and true probability distributions.
Related Concepts
- Loss Function: Explore how Loss Function relates to Cross-Entropy Loss
- Classification: Explore how Classification relates to Cross-Entropy Loss
- Softmax: Explore how Softmax relates to Cross-Entropy Loss
Why It Matters
Understanding Cross-Entropy Loss is crucial for anyone working with training & optimization. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Classification
A supervised learning task where the model predicts discrete class labels (categories) for input data.
Loss Function
A function measuring the difference between model predictions and true values, guiding the training process.
Softmax
An activation function that converts a vector of values into a probability distribution, commonly used for multi-class classification.